From 601ca5c62793af858d32f235219323ca33ad5957 Mon Sep 17 00:00:00 2001 From: "Robert A. McDougal" Date: Wed, 10 Jul 2024 21:23:22 -0400 Subject: [PATCH] initial --- Collaterals_Entrained_values.txt | 12 + Controllers.py | 907 ++ CorticalAxonInterneuron_Connections.txt | 1001 ++ CorticalAxon_Motoneuron_Connections_1.txt | 2501 ++++ CorticalSTN_Connections.txt | 501 + CorticalSomaThalamic_Connections.txt | 101 + Cortical_Axon_I_Kd.mod | 83 + Cortical_Axon_I_Kv.mod | 87 + Cortical_Axon_I_Leak.mod | 43 + Cortical_Axon_I_Na.mod | 97 + Cortical_Basal_Ganglia_Cell_Classes.py | 472 + Cortical_Bias_Current_Signal.npy | Bin 0 -> 640 bytes Cortical_Bias_Signal_Times.npy | Bin 0 -> 640 bytes Cortical_Soma_I_K.mod | 83 + Cortical_Soma_I_Leak.mod | 44 + Cortical_Soma_I_M.mod | 75 + Cortical_Soma_I_Na.mod | 89 + DBS_Amplitude_Interpolation_values.txt | 12 + DBS_Pulse_Width_Interpolation_values.txt | 15 + Depressing_Spinal_Motoneuron_Cell_Classes.py | 1304 ++ Destexhe_Static_AMPA_Synapse.mod | 202 + Destexhe_Static_GABAA_Synapse.mod | 202 + Electrode_Distances.py | 101 + GPeGPe_Connections.txt | 101 + GPeGPi_Connections.txt | 101 + GPeSTN_Connections.txt | 201 + GPe_Stimulation_Order.txt | 100 + GPiThalamic_Connections.txt | 101 + Gfluct.mod | 202 + Global_Variables.py | 35 + Independent_Beta_Spike_Times.npy | Bin 0 -> 4543399 bytes Independent_Motoneuron_Connections_1.txt | 10001 ++++++++++++++++ InterneuronCortical_Connections.txt | 1001 ++ Interneuron_I_K.mod | 86 + Interneuron_I_Leak.mod | 47 + Interneuron_I_Na.mod | 92 + L_Ca_inact.mod | 116 + MU_AP_Shapes.npy | Bin 0 -> 1600928 bytes STNGPe_Connections.txt | 101 + STNGPi_Connections.txt | 101 + STN_xy_pos.txt | 2 + StriatalGPe_Connections.txt | 101 + Striatal_Spike_Times.npy | Bin 0 -> 1874811 bytes SynNoise.mod | 66 + ThalamicCorticalSoma_Connections.txt | 601 + Thalamic_Bias_Current_Signal.npy | Bin 0 -> 2528 bytes Thalamic_Bias_Signal_Times.npy | Bin 0 -> 240128 bytes Thalamic_I_Na_K.mod | 84 + Thalamic_I_T.mod | 76 + Thalamic_I_leak.mod | 44 + Updated_PyNN_Files/common/control.py | 192 + Updated_PyNN_Files/neuron/__init__.py | 132 + Updated_PyNN_Files/neuron/recording.py | 125 + Updated_PyNN_Files/neuron/simulator.py | 633 + .../neuron/standardmodels/electrodes.py | 248 + cortical_collateral_electrode_distances.txt | 100 + cortical_collateral_resistivities.txt | 100 + cortical_collateral_rx.txt | 100 + cortical_xy_pos.txt | 2 + gAxonMU_Connections_after.txt | 1501 +++ gAxonMU_Connections_before.txt | 1501 +++ gh.mod | 1 + kdrRL.mod | 88 + mAHP.mod | 120 + myions.mod | 28 + na3rp.mod | 144 + naps.mod | 105 + pGPeA.mod | 211 + pSTN.mod | 315 + ranstream.hoc | 24 + readme.md | 22 + ...ual_Amplitude_Pulse_Duration_Modulation.py | 1679 +++ run_CBG_MU_Pool_Model_to_SS.py | 1299 ++ xtra.mod | 40 + 74 files changed, 30002 insertions(+) create mode 100644 Collaterals_Entrained_values.txt create mode 100644 Controllers.py create mode 100644 CorticalAxonInterneuron_Connections.txt create mode 100644 CorticalAxon_Motoneuron_Connections_1.txt create mode 100644 CorticalSTN_Connections.txt create mode 100644 CorticalSomaThalamic_Connections.txt create mode 100644 Cortical_Axon_I_Kd.mod create mode 100644 Cortical_Axon_I_Kv.mod create mode 100644 Cortical_Axon_I_Leak.mod create mode 100644 Cortical_Axon_I_Na.mod create mode 100644 Cortical_Basal_Ganglia_Cell_Classes.py create mode 100644 Cortical_Bias_Current_Signal.npy create mode 100644 Cortical_Bias_Signal_Times.npy create mode 100644 Cortical_Soma_I_K.mod create mode 100644 Cortical_Soma_I_Leak.mod create mode 100644 Cortical_Soma_I_M.mod create mode 100644 Cortical_Soma_I_Na.mod create mode 100644 DBS_Amplitude_Interpolation_values.txt create mode 100644 DBS_Pulse_Width_Interpolation_values.txt create mode 100644 Depressing_Spinal_Motoneuron_Cell_Classes.py create mode 100644 Destexhe_Static_AMPA_Synapse.mod create mode 100644 Destexhe_Static_GABAA_Synapse.mod create mode 100644 Electrode_Distances.py create mode 100644 GPeGPe_Connections.txt create mode 100644 GPeGPi_Connections.txt create mode 100644 GPeSTN_Connections.txt create mode 100644 GPe_Stimulation_Order.txt create mode 100644 GPiThalamic_Connections.txt create mode 100644 Gfluct.mod create mode 100644 Global_Variables.py create mode 100644 Independent_Beta_Spike_Times.npy create mode 100644 Independent_Motoneuron_Connections_1.txt create mode 100644 InterneuronCortical_Connections.txt create mode 100644 Interneuron_I_K.mod create mode 100644 Interneuron_I_Leak.mod create mode 100644 Interneuron_I_Na.mod create mode 100644 L_Ca_inact.mod create mode 100644 MU_AP_Shapes.npy create mode 100644 STNGPe_Connections.txt create mode 100644 STNGPi_Connections.txt create mode 100644 STN_xy_pos.txt create mode 100644 StriatalGPe_Connections.txt create mode 100644 Striatal_Spike_Times.npy create mode 100644 SynNoise.mod create mode 100644 ThalamicCorticalSoma_Connections.txt create mode 100644 Thalamic_Bias_Current_Signal.npy create mode 100644 Thalamic_Bias_Signal_Times.npy create mode 100644 Thalamic_I_Na_K.mod create mode 100644 Thalamic_I_T.mod create mode 100644 Thalamic_I_leak.mod create mode 100644 Updated_PyNN_Files/common/control.py create mode 100644 Updated_PyNN_Files/neuron/__init__.py create mode 100644 Updated_PyNN_Files/neuron/recording.py create mode 100644 Updated_PyNN_Files/neuron/simulator.py create mode 100644 Updated_PyNN_Files/neuron/standardmodels/electrodes.py create mode 100644 cortical_collateral_electrode_distances.txt create mode 100644 cortical_collateral_resistivities.txt create mode 100644 cortical_collateral_rx.txt create mode 100644 cortical_xy_pos.txt create mode 100644 gAxonMU_Connections_after.txt create mode 100644 gAxonMU_Connections_before.txt create mode 100644 gh.mod create mode 100644 kdrRL.mod create mode 100644 mAHP.mod create mode 100644 myions.mod create mode 100644 na3rp.mod create mode 100644 naps.mod create mode 100644 pGPeA.mod create mode 100644 pSTN.mod create mode 100644 ranstream.hoc create mode 100644 readme.md create mode 100644 run_CBG_MU_Pool_Model_from_SS_Dual_Amplitude_Pulse_Duration_Modulation.py create mode 100644 run_CBG_MU_Pool_Model_to_SS.py create mode 100644 xtra.mod diff --git a/Collaterals_Entrained_values.txt b/Collaterals_Entrained_values.txt new file mode 100644 index 0000000..0ec70e6 --- /dev/null +++ b/Collaterals_Entrained_values.txt @@ -0,0 +1,12 @@ +0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 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+1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02,1.000000000000000000e+02 diff --git a/Controllers.py b/Controllers.py new file mode 100644 index 0000000..a6ac3bb --- /dev/null +++ b/Controllers.py @@ -0,0 +1,907 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed April 03 14:27:26 2019 + +Description: Controller and scheduler class implementations + +@author: John Fleming, john.fleming@ucdconnect.ie +""" + +import math + +class Controller_Scheduler: + """Controller Scheduler Class""" + + def __init__(self, Controller_Bank=[], Scheduler_Bounds=[], MinParameterValue=0.0, MaxParameterValue=1e9, RampDuration=0.25, Ts=0.02, Scheduler_Label='Scheduler', Controller_Labels=['Tremor_ARV', 'Beta_ARV']): + + # Initial Scheduler Properties + self.Controller_Bank = Controller_Bank # A list of the controllers to be implemented by the scheduler + self.Scheduler_Bounds = Scheduler_Bounds # A list of the parameter bounds for the schduling variable corresponding to each controller + self.MinParameterValue = MinParameterValue # Lower bound on the modulated parameter value + self.MaxParameterValue = MaxParameterValue # Upper bound on the modulated parameter value + self.RampDuration = RampDuration # should be defined in sec, i.e. 0.25 sec + self.Ts = Ts # should be in sec as per above + self.Scheduler_Label = Scheduler_Label + self.Controller_Labels = Controller_Labels + + # Parameter for tracking the controllers utilized from the controller bank + self.Selected_Controller_id = 0 + self.Last_Selected_Controller_id = 0 + self.Transitioning_Controllers = False + + # Calculate how much controller output value will change each controller call + self.maxParameterValueIncrement = (self.MaxParameterValue - self.MinParameterValue)/math.ceil(self.RampDuration/self.Ts) + + # Initialize the output value of the controller + self.LastParameterOutputValue = 0 + + # Lists for tracking scheduler history - Updated every scheduler update call + self.scheduler_state_history = [] + self.scheduler_classification_history = [] + self.scheduler_output_history = [] + self.scheduler_sample_times = [] + + # Lists for tracking controller bank history - Updated when controllers change or method called + self.controller_state_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_error_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_output_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_sample_times = [[] for _ in range(len(self.Controller_Bank))] + + + def clear(self): + """Clears scheduler output values and history""" + + # Reset the selected controller index being used + self.Selected_Controller_id = 0 + self.Last_Selected_Controller_id = 0 + self.Transitioning_Controllers = True # Setting transition to True will force the scheduler to switch off DBS before restarting + + # Clear the output value of the scheduler + self.LastParameterOutputValue = 0 + + # Clear the scheduler history lists + self.scheduler_state_history = [] + self.scheduler_classification_history = [] + self.scheduler_output_history = [] + self.scheduler_sample_times = [] + + # Clear the controller bank history lists + self.controller_state_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_error_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_output_history = [[] for _ in range(len(self.Controller_Bank))] + self.controller_sample_times = [[] for _ in range(len(self.Controller_Bank))] + + def update(self, scheduler_state_value, controller_state_values, current_time): + """Determines which bounds the scheduling variable is in to implement the appropriate controller + + """ + + # 1) Check if scheduler is transitioning between controllers + if self.Transitioning_Controllers == True: + + # Scheduler is transitioning between controllers - + # Ramp down the output parameter at the max rate + ParameterOutputValue = self.LastParameterOutputValue - self.maxParameterValueIncrement + + # Check if the parameter value is <= 0, if so transition is complete + if ParameterOutputValue <= 0.0: + ParameterOutputValue = 0 # Bound the output value at zero + self.Transitioning_Controllers = False # Set the transition flag to off + else: + # 2) Scheduler is not transitioning between controllers - + # 2.1) Check which bounds the scheduling variable is in + for bound_id, bound_values in enumerate(self.Scheduler_Bounds): + # Select the corresponding controller based on the scheduler parameter bounds + if scheduler_state_value >= bound_values[0] and scheduler_state_value < bound_values[1]: + self.Selected_Controller_id = bound_id + + else: + pass + + # Above code assumes that incorrect values are not entered to the scheduler, i.e. a value outside the defined scheduler bounds + + # 2.2) Check if the selected controller has changed from the last scheduler call + if self.Selected_Controller_id == self.Last_Selected_Controller_id: + + # 2.2.1) Controller has not changed since last call, update the output using this controller + ParameterOutputValue = self.Controller_Bank[self.Selected_Controller_id].update(state_value=controller_state_values[self.Selected_Controller_id], current_time=current_time) + + # Update the last selected controller property + self.Last_Selected_Controller_id = self.Selected_Controller_id + self.Transitioning_Controllers = False + + else: + + # 2.2.2) Controller has changed since the last call + # - Get the previously used controller data + self.controller_state_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_state_history()) + self.controller_error_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_error_history()) + self.controller_output_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_output_history()) + self.controller_sample_times[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_sample_times()) + + # And reset the controller for use later on initial change into transition + if self.Transitioning_Controllers == False: + # - Reset the controller for use later + self.Controller_Bank[self.Last_Selected_Controller_id].clear() + # - Set the scheduler transition property to True + self.Transitioning_Controllers = True + else: + pass + + # - Ramp down the output parameter at the max rate + ParameterOutputValue = self.LastParameterOutputValue - self.maxParameterValueIncrement + + # Check if the parameter value is <= 0, if so transition is complete + if ParameterOutputValue <= 0.0: + ParameterOutputValue = 0 # Bound the output value at zero + self.Transitioning_Controllers = False # Set the transition flag to off + + else: + pass + + self.Last_Selected_Controller_id = self.Selected_Controller_id + + # Update the LastParameterOutputValue property to the current output + self.LastParameterOutputValue = ParameterOutputValue + + # Record scheduler state, classification, output, and sample time values + self.scheduler_state_history.append(scheduler_state_value) + self.scheduler_classification_history.append(self.Selected_Controller_id) + self.scheduler_output_history.append(ParameterOutputValue) + self.scheduler_sample_times.append(current_time/1000.0) + + # Return parameter output value + return ParameterOutputValue + + def stop(self): + """Method stops the scheduler and writes current controller data to respective class properties""" + self.controller_state_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_state_history()) + self.controller_error_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_error_history()) + self.controller_output_history[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_output_history()) + self.controller_sample_times[self.Last_Selected_Controller_id].append(self.Controller_Bank[self.Last_Selected_Controller_id].get_sample_times()) + self.Controller_Bank[self.Last_Selected_Controller_id].clear() + + def set_MaxParameterValue(self, MaxParameterValue): + """Sets the upper bound for the controller output""" + self.MaxParameterValue = MaxParameterValue + self.maxParameterValueIncrement = (self.MaxParameterValue - self.MaxParameterValue)/math.ceil(self.RampDuration/self.Ts) + + def set_MinParameterValue(self, MinParameterValue): + """Sets the lower bound for the controller output""" + self.MinParameterValue = MinParameterValue + self.maxParameterValueIncrement = (self.MaxParameterValue - self.MaxParameterValue)/(self.RampDuration/self.Ts) + + def set_RampDuration(self, ramp_duration): + """Sets the how long the controller output takes to reach it's max value""" + self.RampDuration = ramp_duration + self.maxParameterValueIncrement = (self.MaxParameterValue - self.MaxParameterValue)/(self.RampDuration/self.Ts) + + def set_Ts(self, Ts): + """Sets the sampling rate of the controller""" + self.Ts = Ts + self.maxParameterValueIncrement = (self.MaxParameterValue - self.MaxParameterValue)/(self.RampDuration/self.Ts) + + def set_Scheduler_Label(self, label): + """Sets the Scheduler Label""" + self.Scheduler_Label = label + + def set_Controller_Labels(self, label): + """Sets the Controller Labels""" + self.Controller_Labels = label + + def get_scheduler_state_history(self): + return self.scheduler_state_history + + def get_scheduler_classification_history(self): + return self.scheduler_classification_history + + def get_scheduler_output_history(self): + return self.scheduler_output_history + + def get_scheduler_sample_times(self): + return self.scheduler_sample_times + + def get_controller_state_history(self): + return self.controller_state_history + + def get_controller_error_history(self): + return self.controller_error_history + + def get_controller_output_history(self): + return self.controller_output_history + + def get_controller_sample_times(self): + return self.controller_sample_times + + def get_Scheduler_Label(self): + return self.Scheduler_Label + + def get_Controller_Labels(self): + return self.Controller_Labels + +class State_Observer: + """State Observer Class""" + + def __init__(self, Ts=0.0): + # Initial Controller Values + self.Ts = Ts # should be in sec as per above + + # Lists for tracking controller history + self.state_history = [] + self.sample_times = [] + + #self.clear() + + def clear(self): + """Clears current On-Off controller output value and history""" + self.state_history = [] + self.sample_times = [] + + def update(self, state_value, current_time): + # Records current state measurement + self.state_history.append(state_value) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + def setTs(self, Ts): + """Sets the sampling rate of the controller""" + self.Ts = Ts + + def get_state_history(self): + return self.state_history + + def get_sample_times(self): + return self.sample_times + +class Constant_Controller: + """Constant DBS Parameter Controller Class""" + + def __init__(self, SetPoint=0.0, MinValue=0.0, MaxValue=1e9, ConstantValue=0.0, Ts=0.0, units='mA'): + # Initial Controller Values + self.SetPoint = SetPoint + self.MaxValue = MaxValue + self.MinValue = MinValue + self.ConstantValue = ConstantValue + self.Ts = Ts # should be in sec as per above + self.units = units + self.label = ('Constant_Controller/%f%s' % (self.ConstantValue, self.units)) + + # Set output value + self.OutputValue = 0 + + # Lists for tracking controller history + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + def clear(self): + """Clears current On-Off controller output value and history""" + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + self.OutputValue = 0.0 + + def update(self, state_value, current_time): + """Calculates biomarker for constant DBS value + + u = self.ConstantValue + + """ + + # Calculate Error - if SetPoint > 0.0, then normalize error with respect to setpoint + if self.SetPoint==0.0: + error = state_value - self.SetPoint + else: + error = (state_value - self.SetPoint)/self.SetPoint + + # Bound the controller output (between MinValue - MaxValue) + if self.ConstantValue > self.MaxValue: + self.OutputValue = self.MaxValue + elif self.ConstantValue < self.MinValue: + self.OutputValue = self.MinValue + else: + self.OutputValue = self.ConstantValue + + # Record state, error and sample time values + self.state_history.append(state_value) + self.error_history.append(error) + self.output_history.append(self.OutputValue) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + return self.OutputValue + + def setMaxValue(self, max_value): + """Sets the upper bound for the controller output""" + self.MaxValue = max_value + + def setMinValue(self, min_value): + """Sets the lower bound for the controller output""" + self.MinValue = min_value + + def setConstantValue(self, constant_value): + """Sets the constant controller output""" + self.ConstantValue = ConstantValue + + def setTs(self, Ts): + """Sets the sampling rate of the controller""" + self.Ts = Ts + + def setLabel(self, label): + """Sets the label of the controller""" + self.label = label + + def setSetPoint(self, set_point): + self.SetPoint = set_point + + def get_state_history(self): + return self.state_history + + def get_error_history(self): + return self.error_history + + def get_output_history(self): + return self.output_history + + def get_sample_times(self): + return self.sample_times + + def get_label(self): + return self.label + +class ON_OFF_Controller: + """On-Off Controller Class""" + + def __init__(self, SetPoint=0.0, MinValue=0.0, MaxValue=1e9, RampDuration=0.25, Ts=0.02): + # Initial Controller Values + self.SetPoint = SetPoint + self.MaxValue = MaxValue + self.MinValue = MinValue + self.RampDuration = RampDuration # should be defined in sec, i.e. 0.25 sec + self.Ts = Ts # should be in sec as per above + self.label = 'On_Off_Controller' + + # Calculate how much controller output value will change each controller call + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/math.ceil(self.RampDuration/self.Ts) + + # Initialize the output value of the controller + self.LastOutputValue = MinValue + self.OutputValue = MinValue + + # Lists for tracking controller history + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + def clear(self): + """Clears current On-Off controller output value and history""" + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + self.LastOutputValue = 0.0 + self.OutputValue = 0.0 + + def update(self, state_value, current_time): + """Calculates updated controller output value for given reference feedback + + y(t) = y(t-1) + u(t) + + where: + + u(t) = MaxValue / (RampDuration/Ts) if e(t) > SetPoint or -MaxValue / (RampDuration/Ts) if e(t) < SetPoint + + """ + + # Calculate Error - if SetPoint > 0.0, then normalize error with respect to setpoint + if self.SetPoint==0.0: + error = state_value - self.SetPoint + increment = 0.0 + else: + error = (state_value - self.SetPoint)/self.SetPoint + if error > 0.0: + increment = self.OutputValueIncrement + else: + increment = -self.OutputValueIncrement + + # Bound the controller output (between MinValue - MaxValue) + if self.LastOutputValue+increment > self.MaxValue: + self.OutputValue = self.MaxValue + elif self.LastOutputValue+increment < self.MinValue: + self.OutputValue = self.MinValue + else: + self.OutputValue = self.LastOutputValue+increment + + # Record state, error and sample time values + self.state_history.append(state_value) + self.error_history.append(error) + self.output_history.append(self.OutputValue) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + self.LastOutputValue = self.OutputValue + + return self.OutputValue + + def setMaxValue(self, max_value): + """Sets the upper bound for the controller output""" + self.MaxValue = max_value + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setMinValue(self, min_value): + """Sets the lower bound for the controller output""" + self.MinValue = min_value + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setRampDuration(self, ramp_duration): + """Sets the how long the controller output takes to reach it's max value""" + self.RampDuration = ramp_duration + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setTs(self, Ts): + """Sets the sampling rate of the controller""" + self.Ts = Ts + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setLabel(self, label): + """Sets the label of the controller""" + self.label = label + + def setSetPoint(self, set_point): + self.SetPoint = set_point + + def get_state_history(self): + return self.state_history + + def get_error_history(self): + return self.error_history + + def get_output_history(self): + return self.output_history + + def get_sample_times(self): + return self.sample_times + + def get_label(self): + return self.label + +class Dual_Threshold_Controller: + """Dual-Threshold Controller Class""" + + def __init__(self, LowerThreshold=0.0, UpperThreshold=0.1, MinValue=0.0, MaxValue=1e9, RampDuration=0.25, Ts=0.02): + # Initial Controller Values + self.UpperThreshold = UpperThreshold + self.LowerThreshold = LowerThreshold + self.MaxValue = MaxValue + self.MinValue = MinValue + self.RampDuration = RampDuration # should be defined in sec, i.e. 0.25 sec + self.Ts = Ts # should be in sec as per above + self.label = 'Dual_Threshold_Controller' + + # Calculate how much controller output value will change each controller call + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/math.ceil(self.RampDuration/self.Ts) + + # Initialize the output value of the controller + self.LastOutputValue = MinValue + self.OutputValue = MinValue + + # Lists for tracking controller history + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + def clear(self): + """Clears current dual-threshold controller output value and history""" + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + self.LastOutputValue = 0.0 + self.OutputValue = 0.0 + + def update(self, state_value, current_time): + """Calculates updated controller output value for given reference feedback + + if state_value > upper_threshold: + y(t) = y(t-1) + u(t) + elif state_value < lower_threshold: + y(t) = y(t-1) + u(t) + else: + y(t) = y(t-1) + where: + + u(t) = MaxValue / (RampDuration/Ts) if state_value(t) > UpperThreshold or -MaxValue / (RampDuration/Ts) if state_value(t) < LowerThreshold + + """ + + # Check how to update controller value and calculate error with respect to upper/lower threshold + if state_value > self.UpperThreshold: # Increase if above upper threshold + error = (state_value - self.UpperThreshold)/self.UpperThreshold + increment = self.OutputValueIncrement + elif state_value < self.LowerThreshold: # Decrease if below lower threshold + error = (state_value - self.LowerThreshold)/self.LowerThreshold + increment = -self.OutputValueIncrement + else: # Do nothing when within upper and lower thresholds + error = 0 + increment = 0 + + # Bound the controller output (between MinValue - MaxValue) + if self.LastOutputValue+increment > self.MaxValue: + self.OutputValue = self.MaxValue + elif self.LastOutputValue+increment < self.MinValue: + self.OutputValue = self.MinValue + else: + self.OutputValue = self.LastOutputValue+increment + + # Record state, error and sample time values + self.state_history.append(state_value) + self.error_history.append(error) + self.output_history.append(self.OutputValue) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + self.LastOutputValue = self.OutputValue + + return self.OutputValue + + def setUpperThreshold(self, upper_threshold): + """Sets the upper threshold for the measured state""" + self.UpperThreshold = upper_threshold + + def setLowerThreshold(self, lower_threshold): + """Sets the lower threshold for the measured state""" + self.LowerThreshold = lower_threshold + + def setMaxValue(self, max_value): + """Sets the upper bound for the controller output""" + self.MaxValue = max_value + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setMinValue(self, min_value): + """Sets the lower bound for the controller output""" + self.MinValue = min_value + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setRampDuration(self, ramp_duration): + """Sets the how long the controller output takes to reach it's max value""" + self.RampDuration = ramp_duration + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setTs(self, Ts): + """Sets the sampling rate of the controller""" + self.Ts = Ts + self.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts) + + def setLabel(self, label): + """Sets the label of the controller""" + self.label = label + + def setSetPoint(self, set_point): + self.SetPoint = set_point + + def get_state_history(self): + return self.state_history + + def get_error_history(self): + return self.error_history + + def get_output_history(self): + return self.output_history + + def get_sample_times(self): + return self.sample_times + + def get_label(self): + return self.label + +class standard_PID_Controller: + """Standard PID Controller Class""" + + def __init__(self, SetPoint=0.0, Kp=0.0, Ti=0.0, Td=0.0, Ts=0.02, MinValue=0.0, MaxValue=1e9): + + self.SetPoint = SetPoint + self.Kp = Kp + self.Ti = Ti + self.Td = Td + + # Set output value bounds + self.MinValue = MinValue + self.MaxValue = MaxValue + + self.label = "standard_PID_Controller/Kp=%f, Ti=%f, Td=%f" % (self.Kp, self.Ti, self.Td) + + self.Ts = Ts + self.current_time = 0.0 # (sec) + self.last_time = 0.0 + + # Initialize controller terms + self.ITerm = 0.0 + self.DTerm = 0.0 + self.last_error = 0.0 + self.last_OutputValue = 0.0 + + # Initialize the output value of the controller + self.OutputValue = 0.0 + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + def clear(self): + """Clears PID computations and coefficients""" + + self.ITerm = 0.0 + self.DTerm = 0.0 + self.last_error = 0.0 + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + self.OutputValue = 0.0 + + def update(self, state_value, current_time): + """Calculates controller output signal for given reference feedback + + where: + + u(t) = K_p (e(t) + (1/T_i)* \int_{0}^{t} e(t)dt + T_d {de}/{dt}) + + where the error calculated is the tracking error (r(t) - y(t)) + + """ + + # Calculate Error - if SetPoint > 0.0, then normalize error with respect to setpoint + if self.SetPoint==0.0: + error = state_value - self.SetPoint + else: + error = (state_value - self.SetPoint)/self.SetPoint + + self.current_time = current_time/1000.0 # Converting from msec to sec + delta_time = self.Ts + delta_error = error - self.last_error + + self.ITerm += error * delta_time + + self.DTerm = 0.0 + if delta_time > 0: + self.DTerm = delta_error / delta_time + + # Remember last time and last error for next calculation + self.last_time = self.current_time + self.last_error = error + + # Calculate u(t) - catch potential division by zero error + try: + u = self.Kp * (error + ((1.0/self.Ti) * self.ITerm) + (self.Td * self.DTerm)) + except ZeroDivisionError: + u = self.Kp * (error + (0.0 * self.ITerm) + (self.Td * self.DTerm)) + + # Bound the controller output if necessary (between MinValue - MaxValue) + if u > self.MaxValue: + self.OutputValue = self.MaxValue + self.ITerm -= error * delta_time # Back-calculate the integral error + elif u < self.MinValue: + self.OutputValue = self.MinValue + self.ITerm -= error * delta_time # Back-calculate the integral error + else: + self.OutputValue = u + + # Update the last output value + self.last_OutputValue = self.OutputValue + + # Record state, error, y(t), and sample time values + self.state_history.append(state_value) + self.error_history.append(error) + self.output_history.append(self.OutputValue) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + # Return controller output + return self.OutputValue + + def setKp(self, proportional_gain): + """Determine how aggressively the controller reacts to the current error with setting Proportional Gain""" + self.Kp = proportional_gain + self.label = "standard_PID_Controller/Kp=%f, Ti=%f, Td=%f" % (self.Kp, self.Ti, self.Td) + + def setTi(self, Ti): + """Determine how fast the controller integrates the error history by setting Integral Time Constant""" + self.Ti = Ti + self.label = "standard_PID_Controller/Kp=%f, Ti=%f, Td=%f" % (self.Kp, self.Ti, self.Td) + + def setTd(self, Td): + """Determine far into the future the controller predicts future errors setting Derivative Time Constant""" + self.Td = Td + self.label = "standard_PID_Controller/Kp=%f, Ti=%f, Td=%f" % (self.Kp, self.Ti, self.Td) + + def setSetPoint(self, set_point): + """Set target setpoint value""" + self.SetPoint = set_point + + def setMaxValue(self, max_value): + """Sets the upper bound for the controller output""" + self.MaxValue = max_value + + def setMinValue(self, min_value): + """Sets the lower bound for the controller output""" + self.MinValue = min_value + + def get_state_history(self): + return self.state_history + + def get_error_history(self): + return self.error_history + + def get_output_history(self): + return self.output_history + + def get_sample_times(self): + return self.sample_times + + def get_label(self): + return self.label + + +class standard_PID_Controller_Plus_Bias: + """Standard PID Controller plus Constant Output Bias Class""" + + def __init__(self, SetPoint=0.0, Kp=0.0, Ti=0.0, Td=0.0, Bias=0.0, Ts=0.02, MinValue=0.0, MaxValue=1e9): + + self.SetPoint = SetPoint + self.Kp = Kp + self.Ti = Ti + self.Td = Td + self.Bias = Bias + + # Set output value bounds + self.MinValue = MinValue + self.MaxValue = MaxValue + + self.label = "standard_PID_Controller_Plus_Bias/Kp=%f, Ti=%f, Td=%f, Bias=%f" % (self.Kp, self.Ti, self.Td, self.Bias) + + self.Ts = Ts + self.current_time = 0.0 # (sec) + self.last_time = 0.0 + + # Initialize controller terms + self.ITerm = 0.0 + self.DTerm = 0.0 + self.last_error = 0.0 + self.last_OutputValue = 0.0 + + # Initialize the output value of the controller + self.OutputValue = 0.0 + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + def clear(self): + """Clears PID computations and coefficients""" + + self.ITerm = 0.0 + self.DTerm = 0.0 + self.last_error = 0.0 + + self.state_history = [] + self.error_history = [] + self.output_history = [] + self.sample_times = [] + + self.OutputValue = 0.0 + + def update(self, state_value, current_time): + """Calculates controller output signal for given reference feedback + + where: + + u(t) = K_p (e(t) + (1/T_i)* \int_{0}^{t} e(t)dt + T_d {de}/{dt}) + + where the error calculated is the tracking error (r(t) - y(t)) + + """ + + # Calculate Error - if SetPoint > 0.0, then normalize error with respect to setpoint + if self.SetPoint==0.0: + error = state_value - self.SetPoint + else: + error = (state_value - self.SetPoint)/self.SetPoint + + self.current_time = current_time/1000.0 # Converting from msec to sec + delta_time = self.Ts + delta_error = error - self.last_error + + self.ITerm += error * delta_time + + self.DTerm = 0.0 + if delta_time > 0: + self.DTerm = delta_error / delta_time + + # Remember last time and last error for next calculation + self.last_time = self.current_time + self.last_error = error + + # Calculate u(t) - catch potential division by zero error + try: + u = self.Bias + self.Kp * (error + ((1.0/self.Ti) * self.ITerm) + (self.Td * self.DTerm)) + except ZeroDivisionError: + u = self.Bias + self.Kp * (error + (0.0 * self.ITerm) + (self.Td * self.DTerm)) + + # Bound the controller output if necessary (between MinValue - MaxValue) + if u > self.MaxValue: + self.OutputValue = self.MaxValue + self.ITerm -= error * delta_time # Back-calculate the integral error + elif u < self.MinValue: + self.OutputValue = self.MinValue + self.ITerm -= error * delta_time # Back-calculate the integral error + else: + self.OutputValue = u + + # Update the last output value + self.last_OutputValue = self.OutputValue + + # Record state, error, y(t), and sample time values + self.state_history.append(state_value) + self.error_history.append(error) + self.output_history.append(self.OutputValue) + self.sample_times.append(current_time/1000) # Convert from msec to sec + + # Return controller output + return self.OutputValue + + def setKp(self, proportional_gain): + """Determine how aggressively the controller reacts to the current error with setting Proportional Gain""" + self.Kp = proportional_gain + self.label = "standard_PID_Controller_Plus_Bias/Kp=%f, Ti=%f, Td=%f, Bias=%f" % (self.Kp, self.Ti, self.Td, self.Bias) + + def setTi(self, Ti): + """Determine how fast the controller integrates the error history by setting Integral Time Constant""" + self.Ti = Ti + self.label = "standard_PID_Controller_Plus_Bias/Kp=%f, Ti=%f, Td=%f, Bias=%f" % (self.Kp, self.Ti, self.Td, self.Bias) + + def setTd(self, Td): + """Determine far into the future the controller predicts future errors setting Derivative Time Constant""" + self.Td = Td + self.label = "standard_PID_Controller_Plus_Bias/Kp=%f, Ti=%f, Td=%f, Bias=%f" % (self.Kp, self.Ti, self.Td, self.Bias) + + def setBias(self, Bias): + """Set the controller output bias""" + self.Bias = Bias + self.label = "standard_PID_Controller_Plus_Bias/Kp=%f, Ti=%f, Td=%f, Bias=%f" % (self.Kp, self.Ti, self.Td, self.Bias) + + def setSetPoint(self, set_point): + """Set target setpoint value""" + self.SetPoint = set_point + + def setMaxValue(self, max_value): + """Sets the upper bound for the controller output""" + self.MaxValue = max_value + + def setMinValue(self, min_value): + """Sets the lower bound for the controller output""" + self.MinValue = min_value + + def get_state_history(self): + return self.state_history + + def get_error_history(self): + return self.error_history + + def get_output_history(self): + return self.output_history + + def get_sample_times(self): + return self.sample_times + + def get_label(self): + return self.label + diff --git a/CorticalAxonInterneuron_Connections.txt b/CorticalAxonInterneuron_Connections.txt new file mode 100644 index 0000000..0d24f45 --- /dev/null +++ b/CorticalAxonInterneuron_Connections.txt @@ -0,0 +1,1001 @@ +# columns = ['i', 'j', 'weight', 'delay'] +41.0 0.0 0.0010413041117765048 2.0 +60.0 0.0 0.00043024897806383716 2.0 +99.0 0.0 0.0014488821539573684 2.0 +24.0 0.0 0.0002487619149381773 2.0 +8.0 0.0 0.0012753237175490524 2.0 +68.0 0.0 0.0011575794981514068 2.0 +23.0 0.0 0.0009553402567857494 2.0 +69.0 0.0 0.002055679973011373 2.0 +21.0 0.0 0.0009318376032048737 2.0 +29.0 0.0 0.00021017295236970573 2.0 +12.0 1.0 0.0023065014708485215 2.0 +11.0 1.0 0.0021532517596804445 2.0 +19.0 1.0 8.499836050949728e-06 2.0 +82.0 1.0 0.00040190551200787407 2.0 +10.0 1.0 0.00142451686554682 2.0 +97.0 1.0 0.002369275442934426 2.0 +85.0 1.0 0.0004279683758190514 2.0 +92.0 1.0 0.0021828341854822067 2.0 +36.0 1.0 0.000975921043729412 2.0 +18.0 1.0 0.0010516916546532825 2.0 +11.0 2.0 0.002104928533255302 2.0 +59.0 2.0 0.001444114090192193 2.0 +56.0 2.0 0.0017522258432742502 2.0 +84.0 2.0 0.00023598364419982642 2.0 +76.0 2.0 0.0014972263443823281 2.0 +94.0 2.0 0.00015191114468265736 2.0 +87.0 2.0 0.0006378087025077265 2.0 +83.0 2.0 0.0002320865188568086 2.0 +41.0 2.0 0.0008150990965029298 2.0 +25.0 2.0 3.476645863633027e-05 2.0 +22.0 3.0 0.0013342492996431074 2.0 +25.0 3.0 0.0016908736155720283 2.0 +14.0 3.0 0.002369858041516343 2.0 +44.0 3.0 0.00027145285328505464 2.0 +73.0 3.0 0.0024286725285704446 2.0 +2.0 3.0 0.001689581076236433 2.0 +81.0 3.0 0.0006295854921911109 2.0 +30.0 3.0 0.00042338379632704873 2.0 +13.0 3.0 0.0024406932111610503 2.0 +75.0 3.0 0.0020710531193572214 2.0 +52.0 4.0 7.968479438556054e-05 2.0 +95.0 4.0 0.002336153722272902 2.0 +72.0 4.0 0.0013035260238705671 2.0 +45.0 4.0 0.00232695734854255 2.0 +21.0 4.0 0.002129662921816198 2.0 +5.0 4.0 0.0011368178382052576 2.0 +31.0 4.0 0.001437933947798091 2.0 +70.0 4.0 0.0016578150970442736 2.0 +88.0 4.0 0.0009533215604853743 2.0 +51.0 4.0 0.0004905608735635811 2.0 +93.0 5.0 0.001930658686241852 2.0 +49.0 5.0 0.0006582191019039813 2.0 +26.0 5.0 0.0005933414208014162 2.0 +37.0 5.0 0.000183102895258076 2.0 +6.0 5.0 0.0008101355594141072 2.0 +15.0 5.0 0.0023481078776839766 2.0 +0.0 5.0 0.0013938937967137694 2.0 +3.0 5.0 0.0006105352451379861 2.0 +98.0 5.0 6.804256354270344e-05 2.0 +89.0 5.0 0.001308497089804622 2.0 +5.0 6.0 0.0012560226557174381 2.0 +47.0 6.0 0.0009225310250761423 2.0 +49.0 6.0 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b/CorticalSTN_Connections.txt @@ -0,0 +1,501 @@ +# columns = ['i', 'j', 'weight', 'delay'] +41.0 0.0 0.12 1.0 +60.0 0.0 0.12 1.0 +99.0 0.0 0.12 1.0 +24.0 0.0 0.12 1.0 +8.0 0.0 0.12 1.0 +11.0 1.0 0.12 1.0 +82.0 1.0 0.12 1.0 +12.0 1.0 0.12 1.0 +10.0 1.0 0.12 1.0 +19.0 1.0 0.12 1.0 +56.0 2.0 0.12 1.0 +84.0 2.0 0.12 1.0 +11.0 2.0 0.12 1.0 +59.0 2.0 0.12 1.0 +76.0 2.0 0.12 1.0 +25.0 3.0 0.12 1.0 +14.0 3.0 0.12 1.0 +73.0 3.0 0.12 1.0 +22.0 3.0 0.12 1.0 +44.0 3.0 0.12 1.0 +95.0 4.0 0.12 1.0 +72.0 4.0 0.12 1.0 +45.0 4.0 0.12 1.0 +21.0 4.0 0.12 1.0 +52.0 4.0 0.12 1.0 +6.0 5.0 0.12 1.0 +26.0 5.0 0.12 1.0 +37.0 5.0 0.12 1.0 +93.0 5.0 0.12 1.0 +49.0 5.0 0.12 1.0 +5.0 6.0 0.12 1.0 +47.0 6.0 0.12 1.0 +49.0 6.0 0.12 1.0 +51.0 6.0 0.12 1.0 +93.0 6.0 0.12 1.0 +4.0 7.0 0.12 1.0 +82.0 7.0 0.12 1.0 +24.0 7.0 0.12 1.0 +42.0 7.0 0.12 1.0 +3.0 7.0 0.12 1.0 +81.0 8.0 0.12 1.0 +12.0 8.0 0.12 1.0 +22.0 8.0 0.12 1.0 +58.0 8.0 0.12 1.0 +86.0 8.0 0.12 1.0 +26.0 9.0 0.12 1.0 +56.0 9.0 0.12 1.0 +44.0 9.0 0.12 1.0 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1.0 diff --git a/CorticalSomaThalamic_Connections.txt b/CorticalSomaThalamic_Connections.txt new file mode 100644 index 0000000..450a9a1 --- /dev/null +++ b/CorticalSomaThalamic_Connections.txt @@ -0,0 +1,101 @@ +# columns = ['i', 'j', 'weight', 'delay'] +41.0 0.0 0.0 2.0 +12.0 1.0 0.0 2.0 +56.0 2.0 0.0 2.0 +25.0 3.0 0.0 2.0 +95.0 4.0 0.0 2.0 +6.0 5.0 0.0 2.0 +5.0 6.0 0.0 2.0 +4.0 7.0 0.0 2.0 +81.0 8.0 0.0 2.0 +26.0 9.0 0.0 2.0 +55.0 10.0 0.0 2.0 +14.0 11.0 0.0 2.0 +29.0 12.0 0.0 2.0 +90.0 13.0 0.0 2.0 +59.0 14.0 0.0 2.0 +7.0 15.0 0.0 2.0 +32.0 16.0 0.0 2.0 +0.0 17.0 0.0 2.0 +10.0 18.0 0.0 2.0 +12.0 19.0 0.0 2.0 +64.0 20.0 0.0 2.0 +17.0 21.0 0.0 2.0 +61.0 22.0 0.0 2.0 +22.0 23.0 0.0 2.0 +44.0 24.0 0.0 2.0 +2.0 25.0 0.0 2.0 +41.0 26.0 0.0 2.0 +26.0 27.0 0.0 2.0 +32.0 28.0 0.0 2.0 +82.0 29.0 0.0 2.0 +66.0 30.0 0.0 2.0 +9.0 31.0 0.0 2.0 +85.0 32.0 0.0 2.0 +19.0 33.0 0.0 2.0 +99.0 34.0 0.0 2.0 +47.0 35.0 0.0 2.0 +35.0 36.0 0.0 2.0 +48.0 37.0 0.0 2.0 +48.0 38.0 0.0 2.0 +85.0 39.0 0.0 2.0 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+4.0 93.0 0.0 2.0 +20.0 94.0 0.0 2.0 +39.0 95.0 0.0 2.0 +72.0 96.0 0.0 2.0 +85.0 97.0 0.0 2.0 +87.0 98.0 0.0 2.0 +61.0 99.0 0.0 2.0 diff --git a/Cortical_Axon_I_Kd.mod b/Cortical_Axon_I_Kd.mod new file mode 100644 index 0000000..887f74f --- /dev/null +++ b/Cortical_Axon_I_Kd.mod @@ -0,0 +1,83 @@ +TITLE Potassium D-Current for Cortical Neuron Axon + +COMMENT + + Model Reference: + + Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A., + 2011. "Somatic membrane potential and Kv1 channels control spike + repolarization in cortical axon collaterals and presynaptic boutons." + Journal of Neuroscience, 31(43), pp.15490-15498. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_axon_i_kd + USEION k WRITE ik : Using k ion, treat the reversal potential as a parameter and write to ik so the total k current can be tracked + RANGE g_Kd, i_Kd : Potassium current, specific conductance and equilibrium potential +} + +PARAMETER { + ek = -90 (mV) + i_Kd = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_Kd = 0.6e-3 (S/cm2) + tau_m = 1 (ms) + tau_h = 1500 (ms) + V_half_m = -43 (mV) + V_half_h = -67 (mV) + q_m = 8 + q_h = 7.3 + Q_s = 3.209 : Temperature rescaling - Q_10 = 2.3 => Q_s = (Q_10)^((37-23)/10) = 3.209 +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + m_inf + h_inf +} + +STATE { + m h +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = g_Kd*m*h*(v - ek) + i_Kd = ik : Record i_Kv (just this potassium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) + m = m_inf + h = h_inf +} + +DERIVATIVE states { + settables(v) + m' = (m_inf-m)/tau_m + h' = (h_inf-h)/tau_h +} + +PROCEDURE settables(v) { + TABLE m_inf, h_inf FROM -100 TO 100 WITH 400 + + m_inf = 1-(1/(1+exp((v-V_half_m)/q_m))) + h_inf = 1-(1/(1+exp((v-V_half_h)/q_h))) + +} + +UNITSON \ No newline at end of file diff --git a/Cortical_Axon_I_Kv.mod b/Cortical_Axon_I_Kv.mod new file mode 100644 index 0000000..41c2b93 --- /dev/null +++ b/Cortical_Axon_I_Kv.mod @@ -0,0 +1,87 @@ +TITLE Delayed-rectifier Potassium Current for Cortical Neuron Axon + +COMMENT + + Model Reference: + + Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A., + 2011. "Somatic membrane potential and Kv1 channels control spike + repolarization in cortical axon collaterals and presynaptic boutons." + Journal of Neuroscience, 31(43), pp.15490-15498. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_axon_i_kv + USEION k WRITE ik : Using k ion, treat the reversal potential as a parameter and write to ik so the total k current can be tracked + RANGE g_Kv, i_Kv : Potassium current, specific conductance and equilibrium potential +} + +PARAMETER { + ek = -90 (mV) + i_Kv = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_Kv = 0.6e-3 (S/cm2) + Q_s = 3.209 : Temperature rescaling - Q_10 = 2.3 => Q_s = (Q_10)^((37-23)/10) = 3.209 +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + alpha_n + beta_n + n_inf + tau_n (ms) +} + +STATE { + n +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = g_Kv*n*(v - ek) + i_Kv = ik : Record i_Kv (just this potassium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) + n = n_inf +} + +DERIVATIVE states { + settables(v) + n' = (n_inf-n)/tau_n +} + +PROCEDURE settables(v) { + TABLE alpha_n, beta_n, n_inf, tau_n FROM -100 TO 100 WITH 400 + + alpha_n = Q_s*0.01*vtrap(-(v-30),9) + beta_n = Q_s*0.002*vtrap((v-30),9) + + n_inf = alpha_n/(alpha_n+beta_n) + tau_n = 1/(alpha_n+beta_n) +} + +FUNCTION vtrap(x,y) { + if (fabs(x/y) < 1e-6) { + vtrap = y*(1 - x/y/2) + }else{ + vtrap = x/(exp(x/y)-1) + } +} + +UNITSON \ No newline at end of file diff --git a/Cortical_Axon_I_Leak.mod b/Cortical_Axon_I_Leak.mod new file mode 100644 index 0000000..49ee9d6 --- /dev/null +++ b/Cortical_Axon_I_Leak.mod @@ -0,0 +1,43 @@ +TITLE Passive Leak Current for Cortical Neuron Axon Compartments + +COMMENT + + Model Reference: + + Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A., + 2011. "Somatic membrane potential and Kv1 channels control spike + repolarization in cortical axon collaterals and presynaptic boutons." + Journal of Neuroscience, 31(43), pp.15490-15498. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_axon_i_leak + NONSPECIFIC_CURRENT i_l : Declare ASSIGNED variables as RANGE variables so that they can be accessed outside of mod file + RANGE i_l, g_l, e_l : leak current, specific conductance and equilibrium potential +} + +PARAMETER { + g_l = 3.33333e-5 (S/cm2) + e_l = -70 (mV) +} + +ASSIGNED { + v (mV) + i_l (mA/cm2) +} + +BREAKPOINT { + i_l = g_l*(v - e_l) +} \ No newline at end of file diff --git a/Cortical_Axon_I_Na.mod b/Cortical_Axon_I_Na.mod new file mode 100644 index 0000000..ec42a26 --- /dev/null +++ b/Cortical_Axon_I_Na.mod @@ -0,0 +1,97 @@ +TITLE Sodium Current for Cortical Neuron Axon + +COMMENT + + Model Reference: + + Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A., + 2011. "Somatic membrane potential and Kv1 channels control spike + repolarization in cortical axon collaterals and presynaptic boutons." + Journal of Neuroscience, 31(43), pp.15490-15498. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_axon_i_na + USEION na WRITE ina : Using na ion, treat the reversal potential as a parameter and write to ina so the total na current can be tracked + RANGE g_Na, i_Na : Sodium current, specific conductance and equilibrium potential +} + +PARAMETER { + ena = 60 (mV) + i_Na = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_Na = 0.4 (S/cm2) : Default is assuming AIS + Q_s = 3.209 : Temperature rescaling - Q_10 = 2.3 => Q_s = (Q_10)^((37-23)/10) = 3.209 +} + +ASSIGNED { + v (mV) + ina (mA/cm2) + alpha_m + beta_m + alpha_h + beta_h + m_inf + tau_m (ms) + h_inf + tau_h (ms) +} + +STATE { + m h +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ina = g_Na*m*m*m*h*(v - ena) + i_Na = ina : Record i_Na (just this sodium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) + m = m_inf + h = h_inf +} + +DERIVATIVE states { + settables(v) + m' = (m_inf-m)/tau_m + h' = (h_inf-h)/tau_h +} + +PROCEDURE settables(v) { + TABLE alpha_m, beta_m, alpha_h, beta_h, m_inf, tau_m, h_inf, tau_h FROM -100 TO 100 WITH 400 + + alpha_m = Q_s*0.182*vtrap(-(v+30),8) + beta_m = Q_s*0.124*vtrap((v+30),8) + alpha_h = Q_s*0.028*vtrap(-(v+45),6) + beta_h = Q_s*0.0091*vtrap((v+70),6) + + m_inf = alpha_m/(alpha_m+beta_m) + tau_m = 1/(alpha_m+beta_m) + h_inf = 1/(1+exp((v+60)/6.2)) + tau_h = 1/(alpha_h+beta_h) +} + +FUNCTION vtrap(x,y) { + if (fabs(x/y) < 1e-6) { + vtrap = y*(1 - x/y/2) + }else{ + vtrap = x/(exp(x/y)-1) + } +} + +UNITSON \ No newline at end of file diff --git a/Cortical_Basal_Ganglia_Cell_Classes.py b/Cortical_Basal_Ganglia_Cell_Classes.py new file mode 100644 index 0000000..2b3e9a6 --- /dev/null +++ b/Cortical_Basal_Ganglia_Cell_Classes.py @@ -0,0 +1,472 @@ +# -*- coding: utf-8 -*- +""" ------------------------------------------------------------------------------------ + Cortical Basal Ganglia Neurons: file containing classes for defining network neurons + ------------------------------------------------------------------------------------ + + Model References + ------------------------------------------------------------------------------------ + Cortical Pyramical Cell Soma: + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + Cortical Pyramidal Cell Axon: + Foust, A.J., Yu, Y., Popovic, M., Zecevic, D. and McCormick, D.A., + 2011. "Somatic membrane potential and Kv1 channels control spike + repolarization in cortical axon collaterals and presynaptic boutons." + Journal of Neuroscience, 31(43), pp.15490-15498. + + Cortical Interneurons: + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + STN Neurons: + Otsuka, T., Abe, T., Tsukagawa, T. and Song, W.J., 2004. + "Conductance-based model of the voltage-dependent generation + of a plateau potential in subthalamic neurons." + Journal of neurophysiology, 92(1), pp.255-264. + + GP Neurons: + Terman, D., Rubin, J.E., Yew, A.C. and Wilson, C.J., 2002. + "Activity patterns in a model for the subthalamopallidal + network of the basal ganglia." + Journal of Neuroscience, 22(7), pp.2963-2976. + + *Note: The NEURON implementations of the STN and GP neurons are + from the following publication - + Hahn, P.J. and McIntyre, C.C., 2010. + "Modeling shifts in the rate and pattern of subthalamopallidal + network activity during deep brain stimulation." + Journal of computational neuroscience, 28(3), pp.425-441. + + Thalamic Neurons: + Rubin, J.E. and Terman, D., 2004. High frequency stimulation of + the subthalamic nucleus eliminates pathological thalamic rhythmicity + in a computational model. Journal of computational neuroscience, + 16(3), pp.211-235. + + Model Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: 14-02-20: Added additional getters and setters for model + neuron parameters + 16-01-19: Created classes for cell models so they can be + utilized in PyNN. + + Created on Tues Jan 15 12:51:26 2019 + +""" + +from math import pi +from neuron import h +from nrnutils import Mechanism, Section +from pyNN.neuron import NativeCellType +from pyNN.parameters import Sequence +import numpy as np +from scipy import signal + +# Import global variables for GPe DBS +import Global_Variables as GV + +try: + reduce +except NameError: + from functools import reduce + +def _new_property(obj_hierarchy, attr_name): + """ + Returns a new property, mapping attr_name to obj_hierarchy.attr_name. + + For example, suppose that an object of class A has an attribute b which + itself has an attribute c which itself has an attribute d. Then placing + e = _new_property('b.c', 'd') + in the class definition of A makes A.e an alias for A.b.c.d + """ + + def set(self, value): + obj = reduce(getattr, [self] + obj_hierarchy.split('.')) + setattr(obj, attr_name, value) + + def get(self): + obj = reduce(getattr, [self] + obj_hierarchy.split('.')) + return getattr(obj, attr_name) + return property(fset=set, fget=get) + +class Cortical_Neuron(object): + + def __init__(self, **parameters): + + # Create cortical pyramidal neuron soma compartment using Pospischil (2008) single compartment model + self.soma = Section(L=parameters['soma_L'], diam=parameters['soma_diam'], nseg=parameters['soma_nseg'], Ra=parameters['soma_Ra'], cm=parameters['soma_cm'], + mechanisms=(Mechanism('cortical_soma_i_leak'), Mechanism('cortical_soma_i_na'), Mechanism('cortical_soma_i_k'), Mechanism('cortical_soma_i_m'))) + + # Create cortical pyramidal neuron axon compartments using Foust (2011) axon model + self.ais = Section(L=parameters['ais_L'], diam=parameters['ais_diam'], nseg=parameters['ais_nseg'], Ra=parameters['ais_Ra'], cm=parameters['ais_cm'], + mechanisms=(Mechanism('cortical_axon_i_leak', g_l=3.3e-5), Mechanism('cortical_axon_i_na', g_Na=4000e-4), + Mechanism('cortical_axon_i_kv', g_Kv=20e-4), Mechanism('cortical_axon_i_kd', g_Kd=0.015)), parent=self.soma) + + + # Use loop to create myelin and node sections of axon + self.myelin = [] + self.node = [] + for i in np.arange(parameters['num_axon_compartments']): + if i==0: + self.myelin.append(Section(L=parameters['myelin_L'], diam=parameters['myelin_diam'], nseg=11, Ra=parameters['myelin_Ra'], cm=parameters['myelin_cm'], + mechanisms=(Mechanism('cortical_axon_i_leak', g_l=0), Mechanism('cortical_axon_i_na', g_Na=10e-4)), + parent=self.ais)) + else: + self.myelin.append(Section(L=parameters['myelin_L'], diam=parameters['myelin_diam'], nseg=11, Ra=parameters['myelin_Ra'], cm=parameters['myelin_cm'], + mechanisms=(Mechanism('cortical_axon_i_leak', g_l=0), Mechanism('cortical_axon_i_na', g_Na=10e-4)), + parent=self.node[i-1])) + + self.node.append(Section(L=parameters['node_L'], diam=parameters['node_diam'], nseg=parameters['node_nseg'], Ra=parameters['node_Ra'], cm=parameters['node_cm'], + mechanisms=(Mechanism('cortical_axon_i_leak', g_l=0.02), Mechanism('cortical_axon_i_na', g_Na=2800e-4), + Mechanism('cortical_axon_i_kv', g_Kv=5e-4), Mechanism('cortical_axon_i_kd', g_Kd=0.0072)), + parent=self.myelin[i])) + + self.collateral = Section(L=parameters['collateral_L'], diam=parameters['collateral_diam'], nseg=parameters['collateral_nseg'], Ra=parameters['collateral_Ra'], cm=parameters['collateral_cm'], + mechanisms=(Mechanism('cortical_axon_i_leak'), Mechanism('cortical_axon_i_na', g_Na=1333.33333e-4), + Mechanism('cortical_axon_i_kv', g_Kv=10e-4), Mechanism('cortical_axon_i_kd', g_Kd=0.006)), parent=self.node[-3]) # Connect to the 10th node compartment along the main axon + + interneuron_connection_axon_node_index = 5 # Original model version had synaptic connection to interneurons at the 5th axon node, so this is to keep this connection here + middle_index = interneuron_connection_axon_node_index + self.middle_node = self.node[middle_index] + self.middle_myelin = self.myelin[middle_index] + self.end_node = self.node[-1] # Want to specify the last axon node so it can be used to connect to the motoneuron pool + self.end_myelin = self.myelin[-1] + + # Add extracellular and xtra mechanisms to collateral + self.collateral.insert('extracellular') + self.collateral.insert('xtra') + + # Assign default rx values to the segments rx_xtra + # - these values are updated in the main run file + # where rx is calculated as the transfer resistance + # for each collateral segments to the stimulation + # electrode in the homogenous extracellular medium + for seg in self.collateral : + seg.xtra.rx = seg.x*3e-1 + + # Setting pointers to couple extracellular and xtra mechanisms for simulating extracellular DBS + for seg in self.collateral: + h.setpointer(seg._ref_e_extracellular, 'ex', seg.xtra) + h.setpointer(seg._ref_i_membrane, 'im', seg.xtra) + + # Add bias current to neuron model - current amplitude is in terms of original model paper, nA + self.stim = h.IClamp(0.5, sec=self.soma) + self.stim.delay = 0 + self.stim.dur = 1e12 + self.stim.amp = parameters['soma_bias_current_amp'] + + # insert synaptic noise + self.noise = h.SynNoise(0.5, sec=self.soma) + self.noise.f0 = 0 + self.noise.f1 = 0.3 + + # Add AMPA and GABAa synapses to the cell, i.e. add to the soma section + self.AMPA = h.AMPA_S(0.5, sec=self.soma) + self.GABAa = h.GABAa_S(0.5, sec=self.soma) + + # needed for PyNN + self.source = {'soma': self.soma(0.5)._ref_v, 'middle_axon_node': self.middle_node(0.5)._ref_v, 'end_axon_node': self.end_node(0.5)._ref_v, 'collateral': self.collateral(0.5)._ref_v} + self.source_section = {'soma': self.soma, 'middle_axon_node': self.middle_node, 'end_axon_node': self.end_node, 'collateral': self.collateral} + self.rec = h.NetCon(self.source['collateral'], None, sec=self.source_section['collateral']) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.traces = {} + self.recording_time = False + self.parameter_names = () + + def soma_area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + + def _set_collateral_rx(self, sequence_values): + rx_values = sequence_values.value + for ii, seg in enumerate(self.collateral): + seg.xtra.rx = rx_values[ii] + + def _get_collateral_rx(self): + rx_values = np.zeros((1,self.collateral.nseg)) + for i, seg in enumerate(self.collateral): + rx_values[0,i] = seg.xtra.rx + print(Sequence(rx_values.flatten())) + + collateral_rx = property(fget=_get_collateral_rx, fset=_set_collateral_rx) + + +class Cortical_Neuron_Type(NativeCellType): + + default_parameters = {'soma_L': 35, 'soma_diam': 25, 'soma_nseg': 1, 'soma_Ra': 150, 'soma_cm': 1, 'soma_bias_current_amp': 0.12, + 'ais_L': 20, 'ais_diam': 1.2, 'ais_nseg': 5, 'ais_Ra': 150, 'ais_cm': 0.8, + 'myelin_L': 500, 'myelin_L_0': 80, 'myelin_diam': 1.4, 'myelin_Ra': 150, 'myelin_cm': 0.04, + 'node_L': 2, 'node_diam': 1.2, 'node_nseg': 1,'node_Ra': 150, 'node_cm': 0.8, + 'collateral_L': 500, 'collateral_diam': 0.5, 'collateral_nseg': 11, 'collateral_Ra': 150, 'collateral_cm': 0.8, + 'num_axon_compartments': 12} + + # Define initial vector of transfer resistances for the collateral segments + initial_collateral_rx = np.zeros((1,default_parameters['collateral_nseg'])).flatten() + initial_collateral_rx_Sequence = Sequence(initial_collateral_rx) + default_parameters['collateral_rx'] = initial_collateral_rx_Sequence + + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v','collateral(0.5).v', 'collateral(0.5).i_membrane_', 'ais(0.5).v', 'middle_node(0.5).v' , 'middle_myelin(0.5).v', 'end_node(0.5).v' , 'end_myelin(0.5).v', 'AMPA.i', 'GABAa.i'] + units = {'soma(0.5).v' : 'mV', 'collateral(0.5).v': 'mV', 'collateral(0.5).i_membrane_': 'nA', 'ais(0.5).v': 'mV', 'middle_node(0.5).v': 'mV' , 'middle_myelin(0.5).v': 'mV', 'end_node(0.5).v': 'mV' , 'end_myelin(0.5).v': 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'} + receptor_types = ['AMPA', 'GABAa'] + model = Cortical_Neuron + +class Interneuron(object): + + def __init__(self, **parameters): + + # Create single compartment Destexhe Interneuron cell section, i.e. soma section + self.soma = Section(L=parameters['L'], diam=parameters['diam'], nseg=parameters['nseg'], Ra=parameters['Ra'], cm=parameters['cm'], + mechanisms=(Mechanism('interneuron_i_leak'), Mechanism('interneuron_i_na'), Mechanism('interneuron_i_k'))) + + # Add bias current to neuron model - current amplitude is in terms of original model paper, nA + self.stim = h.IClamp(0.5, sec=self.soma) + self.stim.delay = 0 + self.stim.dur = 1e12 + self.stim.amp = parameters['bias_current_amp'] # nA + + # insert synaptic noise + self.noise = h.SynNoise(0.5, sec=self.soma) + self.noise.f0 = 0 + self.noise.f1 = 0.3 + + # Add AMPA and GABAa synapses to the cell, i.e. add to the soma section + self.AMPA = h.AMPA_S(0.5, sec=self.soma) + self.GABAa = h.GABAa_S(0.5, sec=self.soma) + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.source_section) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm', 'bias_current_amp') + self.traces = {} + self.recording_time = False + + L = _new_property('soma', 'L') + diam = _new_property('soma', 'diam') + nseg = _new_property('soma', 'nseg') + Ra = _new_property('soma', 'Ra') + cm = _new_property('soma', 'cm') + bias_current_amp = _new_property('stim', 'amp') + + def area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + +class Interneuron_Type(NativeCellType): + default_parameters = {'L': 35, 'diam': 25, 'nseg': 1, 'Ra': 150, 'cm': 1, 'bias_current_amp': 0.25} + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v', 'AMPA.i', 'GABAa.i'] + units = {'soma(0.5).v' : 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'} + receptor_types = ['AMPA', 'GABAa'] + model = Interneuron + +class STN_Neuron(object): + + def __init__(self, **parameters): + + # Create single compartment Otsuka STN cell section, i.e. soma section + self.soma = Section(L=parameters['L'], diam=parameters['diam'], nseg=parameters['nseg'], Ra=parameters['Ra'], cm=parameters['cm'], + mechanisms=[Mechanism('myions'), Mechanism('stn', gnabar=49e-3, gkdrbar=57e-3, gkabar=5e-3, gkcabar=1.0e-3, gcalbar=15e-3, + gcatbar=5e-3, kca=2, gl=0.35e-3)]) + # Initialize ion concentrations + h("cai0_ca_ion = 5e-6 ") + h("cao0_ca_ion = 2") + h("ki0_k_ion = 105") + h("ko0_k_ion = 3") + h("nao0_na_ion = 108") + h("nai0_na_ion = 10") + + # Add bias current to neuron model + self.stim = h.IClamp(0.5, sec=self.soma) + self.stim.delay = 0 + self.stim.dur = 1e12 + self.stim.amp = parameters['bias_current'] # bias current density (nA) + + # Add AMPA and GABAa synapses to the cell, i.e. add to the soma section + self.AMPA = h.AMPA_S(0.5, sec=self.soma) + self.GABAa = h.GABAa_S(0.5, sec=self.soma) + + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.soma) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm', 'bias_current') + self.traces = {} + self.recording_time = False + + L = _new_property('soma', 'L') + diam = _new_property('soma', 'diam') + nseg = _new_property('soma', 'nseg') + Ra = _new_property('soma', 'Ra') + cm = _new_property('soma', 'cm') + bias_current_amp = _new_property('stim', 'amp') + + def area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + + +class STN_Neuron_Type(NativeCellType): + default_parameters = {'L': 60, 'diam': 60, 'nseg': 1, 'Ra': 200, 'cm': 1, 'bias_current': 0.0, 'num_AMPA_Synapses': 5, 'num_GABAa_Synapses': 5} + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v', 'AMPA.i', 'GABAa.i'] + units = {'soma(0.5).v' : 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'} + receptor_types = ['AMPA', 'GABAa'] + model = STN_Neuron + +class GP_Neuron(object): + + def __init__(self, **parameters): + + # Create single compartment Rubin and Terman GP cell section, i.e. soma section + self.soma = Section(L=parameters['L'], diam=parameters['diam'], nseg=parameters['nseg'], Ra=parameters['Ra'], cm=parameters['cm'], + mechanisms=[Mechanism('myions'), Mechanism('GPeA', gnabar=0.04, gkdrbar=0.0042, gkcabar=0.1e-3, + gcatbar=6.7e-5, kca=2, gl=4e-5)]) + + # Initialize ion concentrations + h("cai0_ca_ion = 5e-6 ") + h("cao0_ca_ion = 2") + h("ki0_k_ion = 105") + h("ko0_k_ion = 3") + h("nao0_na_ion = 108") + h("nai0_na_ion = 10") + + # insert current source + self.stim = h.IClamp(0.5, sec=self.soma) + self.stim.delay = 0 + self.stim.dur = 1e12 + self.stim.amp = parameters['bias_current'] + + # Add DBS stimulation current to neuron model + self.DBS_stim = h.IClamp(0.5, sec=self.soma) + self.DBS_stim.delay = 0 + self.DBS_stim.dur = 1e9 + self.DBS_stim.amp = 0 + + # Append the DBS stimulation iclamps to global list + GV.GPe_stimulation_iclamps.append(self.DBS_stim) + + # Add AMPA and GABAa synapses to the cell, i.e. add to the soma section + self.AMPA = h.AMPA_S(0.5, sec=self.soma) + self.GABAa = h.GABAa_S(0.5, sec=self.soma) + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.soma) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm', 'bias_current_density') + self.traces = {} + self.recording_time = False + + L = _new_property('soma', 'L') + diam = _new_property('soma', 'diam') + nseg = _new_property('soma', 'nseg') + Ra = _new_property('soma', 'Ra') + cm = _new_property('soma', 'cm') + bias_current = _new_property('stim', 'amp') + + def area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + + +class GP_Neuron_Type(NativeCellType): + default_parameters = {'L': 60, 'diam': 60, 'nseg': 1, 'Ra': 200, 'cm': 1.0, 'bias_current': 0.03} + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v'] + units = {'soma(0.5).v' : 'mV'} + receptor_types = ['AMPA', 'GABAa'] + model = GP_Neuron + +class Thalamic_Neuron(object): + + def __init__(self, **parameters): + + # Create single compartment Rubin and Terman Thalamic cell section, i.e. soma section + self.soma = Section(L=parameters['L'], diam=parameters['diam'], nseg=parameters['nseg'], Ra=parameters['Ra'], cm=parameters['cm'], + mechanisms=(Mechanism('thalamic_i_leak'), Mechanism('thalamic_i_na_k'), Mechanism('thalamic_i_t'))) + + """ + # Note: Thalamic current has no bias current in original paper, i.e. bias_current_density = 0 + # Can be added in if required. + # insert current source + self.stim = h.IClamp(0.5, sec=self.soma) + self.stim.delay = 0 + self.stim.dur = 1e12 + self.stim.amp = parameters['bias_current_density']*(self.area())*(0.001) # (0.001 or 1e-3) is conversion factor so pA -> nA + """ + + # insert synaptic noise + self.noise = h.SynNoise(0.5, sec=self.soma) + self.noise.f0 = 0 + self.noise.f1 = 0.3 + + # Add AMPA and GABAa synapses to the cell, i.e. add to the soma section + self.AMPA = h.AMPA_S(0.5, sec=self.soma) + self.GABAa = h.GABAa_S(0.5, sec=self.soma) + + # Set threshold netcons on the neuron + self.soma.v_thresh = parameters['v_thres'] + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.soma) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm', 'bias_current_density', 'v_thres') + self.traces = {} + self.recording_time = False + + L = _new_property('soma', 'L') + diam = _new_property('soma', 'diam') + nseg = _new_property('soma', 'nseg') + Ra = _new_property('soma', 'Ra') + cm = _new_property('soma', 'cm') + + def get_threshold(self): + return self.soma.v_thresh + + def area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + + +class Thalamic_Neuron_Type(NativeCellType): + default_parameters = {'L': 100, 'diam': 100, 'nseg': 1, 'Ra': 150, 'cm': 100, 'bias_current_amplitude': 0, 'v_thres': -20} + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v', 'AMPA.i', 'GABAa.i'] + units = {'soma(0.5).v' : 'mV', 'AMPA.i': 'nA', 'GABAa.i': 'nA'} + receptor_types = ['AMPA', 'GABAa'] + model = Thalamic_Neuron \ No newline at end of file diff --git a/Cortical_Bias_Current_Signal.npy b/Cortical_Bias_Current_Signal.npy new file mode 100644 index 0000000000000000000000000000000000000000..3a8b379079720b48472b5e779aff834fa4383c2f GIT binary patch literal 640 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= mXCxM+0{I$dCOVor3bhL41FqXY>$3KgY_?~Bf>Hj+3J(AzIkzod@L3#LEVymjEdzPLbi4TQeB z@2x|6T3VW$>^m^cX7J7-w*S!A8w()xjLq-B`c6o_2h$z8?;X~=Kdid42trTU`rhH2 zMoj-Bo)2K!N$i7zF%Xn{K%F12ViiwdcT9v ztG`3U8#F$G)jcr$2$mO5fY9ZcA02?!9qOG3q2JE>=)ei|kL+m(U3=xDgWJO@5jplx bV7gA=lS2T=d|L>;+U=9W7oY)F&7T|qN{t?9 literal 0 HcmV?d00001 diff --git a/Cortical_Soma_I_K.mod b/Cortical_Soma_I_K.mod new file mode 100644 index 0000000..5062318 --- /dev/null +++ b/Cortical_Soma_I_K.mod @@ -0,0 +1,83 @@ +TITLE Delayed-rectifier Potassium Current for Cortical Neuron Soma + +COMMENT + + Model Reference: + + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_soma_i_k + USEION k WRITE ik : Using k ion, treat the reversal potential as a parameter and write to ik so the total k current can be tracked + RANGE g_K, i_K : Potassium current, specific conductance and equilibrium potential +} + +PARAMETER { + ek = -100 (mV) + i_K = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_K = 5e-3 (S/cm2) + V_T = -55(mV) +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + alpha_n + beta_n +} + +STATE { + n +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = g_K*n*n*n*n*(v - ek) + i_K = ik : Record i_K (just this potassium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) : ** Need to double check these intials are correct + n = 0 +} + +DERIVATIVE states { + settables(v) + n' = alpha_n*(1-n)-beta_n*n +} + +PROCEDURE settables(v) { + TABLE alpha_n, beta_n DEPEND V_T FROM -100 TO 100 WITH 400 + alpha_n = 0.032 * vtrap(-(v-V_T-15), 5) + beta_n = 0.5*exp(-(v-V_T-10)/40) +} + +FUNCTION vtrap(x,y) { + if (fabs(x/y) < 1e-6) { + vtrap = y*(1 - x/y/2) + }else{ + vtrap = x/(exp(x/y)-1) + } +} + + +UNITSON \ No newline at end of file diff --git a/Cortical_Soma_I_Leak.mod b/Cortical_Soma_I_Leak.mod new file mode 100644 index 0000000..eb579d1 --- /dev/null +++ b/Cortical_Soma_I_Leak.mod @@ -0,0 +1,44 @@ +TITLE Passive Leak Current for Cortical Neuron Soma + +COMMENT + + Model Reference: + + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_soma_i_leak + NONSPECIFIC_CURRENT i_l : Declare ASSIGNED variables as RANGE variables so that they can be accessed outside of mod file + RANGE i_l, g_l, e_l : leak current, specific conductance and equilibrium potential +} + +PARAMETER { + g_l = 0.1e-3 (S/cm2) + e_l = -70 (mV) +} + +ASSIGNED { + v (mV) + i_l (mA/cm2) +} + +BREAKPOINT { + i_l = g_l*(v - e_l) +} \ No newline at end of file diff --git a/Cortical_Soma_I_M.mod b/Cortical_Soma_I_M.mod new file mode 100644 index 0000000..2099ed5 --- /dev/null +++ b/Cortical_Soma_I_M.mod @@ -0,0 +1,75 @@ +TITLE Slow non-inactivating Potassium Current for Cortical Neuron Soma + +COMMENT + + Model Reference: + + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_soma_i_m + USEION k WRITE ik : Using k ion, treat the reversal potential as a parameter and write to ik so the total k current can be tracked + RANGE g_M, i_M : Potassium current, specific conductance and equilibrium potential +} + +PARAMETER { + ek = -100 (mV) + i_M = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_M = 7e-5 (S/cm2) + tau_max = 1000 (ms) +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + p_inf + tau_p (ms) +} + +STATE { + p +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = g_M*p*(v - ek) + i_M = ik : Record i_M (just this potassium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) + p = p_inf +} + +DERIVATIVE states { + settables(v) + p' = (p_inf - p)/tau_p +} + +PROCEDURE settables(v) { + TABLE p_inf, tau_p DEPEND tau_max FROM -100 TO 100 WITH 400 + + p_inf = 1/(1+exp(-(v+35)/10)) + tau_p = tau_max/(3.3*exp((v+35)/20)+exp(-(v+35)/20)) +} + +UNITSON \ No newline at end of file diff --git a/Cortical_Soma_I_Na.mod b/Cortical_Soma_I_Na.mod new file mode 100644 index 0000000..631e689 --- /dev/null +++ b/Cortical_Soma_I_Na.mod @@ -0,0 +1,89 @@ +TITLE Sodium Current for Cortical Neuron Soma + +COMMENT + + Model Reference: + + Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., + Bal, T., Frégnac, Y., Markram, H. and Destexhe, A., 2008. + "Minimal Hodgkin–Huxley type models for different classes of + cortical and thalamic neurons." + Biological cybernetics, 99(4-5), pp.427-441. + + Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18 + + Edits: + +ENDCOMMENT + + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (S) = (siemens) +} + +NEURON { + SUFFIX cortical_soma_i_na + USEION na WRITE ina : Using na ion, treat the reversal potential as a parameter and write to ik so the total k current can be tracked + RANGE g_Na, i_Na : Sodium current, specific conductance and equilibrium potential +} + +PARAMETER { + ena = 50 (mV) + i_Na = 0.0 (mA/cm2) : Parameter to record this current separately to total sodium current + g_Na = 50e-3 (S/cm2) + V_T = -55(mV) +} + +ASSIGNED { + v (mV) + ina (mA/cm2) + alpha_m + beta_m + alpha_h + beta_h +} + +STATE { + m h +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ina = g_Na*m*m*m*h*(v - ena) + i_Na = ina : Record i_Na (just this sodium current) to check it is working +} + +UNITSOFF + +INITIAL { + settables(v) : ** Need to double check these intials are correct + m = 0 + h = 0 +} + +DERIVATIVE states { + settables(v) + m' = alpha_m*(1-m)-beta_m*m + h' = alpha_h*(1-h)-beta_h*h +} + +PROCEDURE settables(v) { + TABLE alpha_m, beta_m, alpha_h, beta_h DEPEND V_T FROM -100 TO 100 WITH 400 + + alpha_m = 0.32*vtrap(-(v-V_T-13),4) + beta_m = 0.28*vtrap((v-V_T-40),5) + alpha_h = 0.128*exp(-(v-V_T-17)/18) + beta_h = 4/(1+exp(-(v-V_T-40)/5)) +} + +FUNCTION vtrap(x,y) { + if (fabs(x/y) < 1e-6) { + vtrap = y*(1 - x/y/2) + }else{ + vtrap = x/(exp(x/y)-1) + } +} + +UNITSON \ No newline at end of file diff --git a/DBS_Amplitude_Interpolation_values.txt b/DBS_Amplitude_Interpolation_values.txt new file mode 100644 index 0000000..60783c6 --- /dev/null +++ b/DBS_Amplitude_Interpolation_values.txt @@ -0,0 +1,12 @@ +0.000000000000000000e+00 +2.500000000000000000e-01 +5.000000000000000000e-01 +7.500000000000000000e-01 +1.000000000000000000e+00 +1.250000000000000000e+00 +1.500000000000000000e+00 +1.750000000000000000e+00 +2.000000000000000000e+00 +3.000000000000000000e+00 +4.000000000000000000e+00 +5.000000000000000000e+00 diff --git a/DBS_Pulse_Width_Interpolation_values.txt b/DBS_Pulse_Width_Interpolation_values.txt new file mode 100644 index 0000000..194fd87 --- /dev/null +++ b/DBS_Pulse_Width_Interpolation_values.txt @@ -0,0 +1,15 @@ +5.999999999999999778e-02 +6.999999999999999278e-02 +8.000000000000000167e-02 +8.999999999999999667e-02 +1.000000000000000056e-01 +1.100000000000000006e-01 +1.199999999999999956e-01 +1.300000000000000044e-01 +1.400000000000000133e-01 +1.500000000000000222e-01 +1.600000000000000033e-01 +1.700000000000000122e-01 +1.800000000000000211e-01 +1.900000000000000022e-01 +2.000000000000000111e-01 diff --git a/Depressing_Spinal_Motoneuron_Cell_Classes.py b/Depressing_Spinal_Motoneuron_Cell_Classes.py new file mode 100644 index 0000000..e432b3c --- /dev/null +++ b/Depressing_Spinal_Motoneuron_Cell_Classes.py @@ -0,0 +1,1304 @@ +# -*- coding: utf-8 -*- +""" +Created on April 03 14:27:26 2020 + +Depressing_Spinal_Motoneuron_Cell_Classes.py + +This file contains the Spinal_Motoneuron class describing a five-compartment +motoneuron structure (single soma and four dendritic compartments) each +containing hodgkin-huxley type ionic mechanisms. Each dendritic compartment +receives a synaptic input due to correlated and independent synaptic currents. + +Implemented by: John Fleming, john.fleming@ucdconnect.ie, + Sageanne Senneff, sageanne.senneff@ucdconnect.ie + +Edits: 06-11-19 : John Fleming - Converted Sageanne's implementation of the motoneuron NEURON model into correct format for PyNN + 04-06-20 : John Fleming - Updated the motoneuron class to have synapse mechanisms for correlated and independent synaptic inputs on the dendrites + +""" +from neuron import h +from nrnutils import Mechanism, Section +from pyNN.neuron import NativeCellType +from pyNN.parameters import Sequence +import numpy as np + +# Import global variables for generating the seeds for the dendritic noise +import Global_Variables as GV + +# Load ranstream hoc template for generating random streams +h.load_file("ranstream.hoc") + +try: + reduce +except NameError: + from functools import reduce + +def _new_property(obj_hierarchy, attr_name): + """ + Returns a new property, mapping attr_name to obj_hierarchy.attr_name. + + For example, suppose that an object of class A has an attribute b which + itself has an attribute c which itself has an attribute d. Then placing + e = _new_property('b.c', 'd') + in the class definition of A makes A.e an alias for A.b.c.d + """ + + def set(self, value): + obj = reduce(getattr, [self] + obj_hierarchy.split('.')) + setattr(obj, attr_name, value) + + def get(self): + obj = reduce(getattr, [self] + obj_hierarchy.split('.')) + return getattr(obj, attr_name) + return property(fset=set, fget=get) + +class Spinal_Motoneuron_Synaptic_Bombardment_Point_Process(object): + + def __init__(self, **parameters): + + # Create Soma Compartments + self.soma = Section(diam = parameters['soma_diam'], L = parameters['soma_L'], Ra = parameters['soma_Ra'], cm = parameters['soma_cm'], + mechanisms = (Mechanism('na3rp', gbar = parameters['soma_na3rp_gbar'], sh = parameters['soma_na3rp_sh'], ar = parameters['soma_na3rp_ar'], qinf = parameters['soma_na3rp_qinf'], thinf = parameters['soma_na3rp_thinf']), + Mechanism('naps', gbar = parameters['soma_naps_gbar'], sh = parameters['soma_naps_sh'], ar = parameters['soma_naps_ar'], vslope = parameters['soma_naps_vslope'], asvh = parameters['soma_naps_asvh'], bsvh = parameters['soma_naps_bsvh']), + Mechanism('kdrRL', gMax = parameters['soma_kdrRL_gMax'], tmin = parameters['soma_kdrRL_tmin'], taumax = parameters['soma_kdrRL_taumax'], mVh = parameters['soma_kdrRL_mVh']), + Mechanism('mAHP', gcamax = parameters['soma_mAHP_gcamax'], gkcamax = parameters['soma_mAHP_gkcamax'], taur = parameters['soma_mAHP_taur'], mtauca = parameters['soma_mAHP_mtauca'], mvhalfca = parameters['soma_mAHP_mvhalfca']))) + + # Insert passive mechanism in soma + self.soma.insert('pas') + for seg in self.soma: + seg.pas.g = parameters['soma_pas_g'] + seg.pas.e = parameters['soma_pas_e'] + + # Insert h mechanism in soma + self.soma.insert('gh') + for seg in self.soma: + seg.gh.ghbar = parameters['soma_gh_ghbar'] + seg.gh.half = parameters['soma_gh_half'] + seg.gh.htau = parameters['soma_gh_htau'] + + # Set threshold netcons on the neuron + self.soma.v_thresh = parameters['v_thres'] + + # Add bias current to MU soma - current amplitude is in terms of original model paper, nA + self.soma_bias = h.IClamp(0.5, sec=self.soma) + self.soma_bias.delay = 0 + self.soma_bias.dur = 1e12 + self.soma_bias.amp = parameters['soma_bias_current_amp'] + + # Create Dendritic Compartments + self.d1 = Section(diam = parameters['dendrite_diam'], L = parameters['dendrite_L'], Ra = parameters['dendrite_Ra'], cm = parameters['dendrite_cm'], + mechanisms = (Mechanism('pas', g = parameters['dendrite_pas_g'], e = parameters['dendrite_pas_e']), + Mechanism('gh', ghbar = parameters['dendrite_gh_ghbar'], half = parameters['dendrite_gh_half'], htau = parameters['dendrite_gh_htau']))) + + self.d2 = Section(diam = parameters['dendrite_diam'], L = parameters['dendrite_L'], Ra = parameters['dendrite_Ra'], cm = parameters['dendrite_cm'], + mechanisms = (Mechanism('pas', g = parameters['dendrite_pas_g'], e = parameters['dendrite_pas_e']), + Mechanism('gh', ghbar = parameters['dendrite_gh_ghbar'], half = parameters['dendrite_gh_half'], htau = parameters['dendrite_gh_htau']))) + + self.d3 = Section(diam = parameters['dendrite_diam'], L = parameters['dendrite_L'], Ra = parameters['dendrite_Ra'], cm = parameters['dendrite_cm'], + mechanisms = (Mechanism('pas', g = parameters['dendrite_pas_g'], e = parameters['dendrite_pas_e']), + Mechanism('gh', ghbar = parameters['dendrite_gh_ghbar'], half = parameters['dendrite_gh_half'], htau = parameters['dendrite_gh_htau']))) + + self.d4 = Section(diam = parameters['dendrite_diam'], L = parameters['dendrite_L'], Ra = parameters['dendrite_Ra'], cm = parameters['dendrite_cm'], + mechanisms = (Mechanism('pas', g = parameters['dendrite_pas_g'], e = parameters['dendrite_pas_e']), + Mechanism('gh', ghbar = parameters['dendrite_gh_ghbar'], half = parameters['dendrite_gh_half'], htau = parameters['dendrite_gh_htau']))) + + # Connect the dendritic compartments to their respective parts of the motoneuron soma + # Note connect only takes one parameter + self.d1.connect(self.soma(1)) + self.d2.connect(self.soma(1)) + self.d3.connect(self.soma(0)) + self.d4.connect(self.soma(0)) + + # Insert the persistent inward currents on the dendrites & set their values + # Dendrite 1: + self.d1.insert('L_Ca_inact') + for seg in self.d1: + seg.L_Ca_inact.gcabar = parameters['dendrite_1_L_Ca_inact_gcabar'] + seg.L_Ca_inact.theta_m = parameters['dendrite_L_Ca_inact_theta_m'] + seg.L_Ca_inact.tau_m = parameters['dendrite_L_Ca_inact_tau_m'] + seg.L_Ca_inact.theta_h = parameters['dendrite_L_Ca_inact_theta_h'] + seg.L_Ca_inact.tau_h = parameters['dendrite_L_Ca_inact_tau_h'] + seg.L_Ca_inact.kappa_h = parameters['dendrite_L_Ca_inact_kappa_h'] + + # Dendrite 2: + self.d2.insert('L_Ca_inact') + for seg in self.d2: + seg.L_Ca_inact.gcabar = parameters['dendrite_2_L_Ca_inact_gcabar'] + seg.L_Ca_inact.theta_m = parameters['dendrite_L_Ca_inact_theta_m'] + seg.L_Ca_inact.tau_m = parameters['dendrite_L_Ca_inact_tau_m'] + seg.L_Ca_inact.theta_h = parameters['dendrite_L_Ca_inact_theta_h'] + seg.L_Ca_inact.tau_h = parameters['dendrite_L_Ca_inact_tau_h'] + seg.L_Ca_inact.kappa_h = parameters['dendrite_L_Ca_inact_kappa_h'] + + # Dendrite 3: + self.d3.insert('L_Ca_inact') + for seg in self.d3: + seg.L_Ca_inact.gcabar = parameters['dendrite_3_L_Ca_inact_gcabar'] + seg.L_Ca_inact.theta_m = parameters['dendrite_L_Ca_inact_theta_m'] + seg.L_Ca_inact.tau_m = parameters['dendrite_L_Ca_inact_tau_m'] + seg.L_Ca_inact.theta_h = parameters['dendrite_L_Ca_inact_theta_h'] + seg.L_Ca_inact.tau_h = parameters['dendrite_L_Ca_inact_tau_h'] + seg.L_Ca_inact.kappa_h = parameters['dendrite_L_Ca_inact_kappa_h'] + + # Dendrite 4: + self.d4.insert('L_Ca_inact') + for seg in self.d4: + seg.L_Ca_inact.gcabar = parameters['dendrite_4_L_Ca_inact_gcabar'] + seg.L_Ca_inact.theta_m = parameters['dendrite_L_Ca_inact_theta_m'] + seg.L_Ca_inact.tau_m = parameters['dendrite_L_Ca_inact_tau_m'] + seg.L_Ca_inact.theta_h = parameters['dendrite_L_Ca_inact_theta_h'] + seg.L_Ca_inact.tau_h = parameters['dendrite_L_Ca_inact_tau_h'] + seg.L_Ca_inact.kappa_h = parameters['dendrite_L_Ca_inact_kappa_h'] + + # Add iclamps for playing in dendritic bias currents + # Dendrite 1: + self.d1_bias_current = h.IClamp(0.5, sec=self.d1) # IClamp used to play bias current into the dendrite + self.d1_bias_current.delay = 0 + self.d1_bias_current.dur = 1e12 + self.d1_bias_current.amp = parameters['dendrite_bias_current_amp'] # Updated in main script to play a ramped bias current through this IClamp + + # Dendrite 2: + self.d2_bias_current = h.IClamp(0.5, sec=self.d2) # IClamp used to play bias current into the dendrite + self.d2_bias_current.delay = 0 + self.d2_bias_current.dur = 1e12 + self.d2_bias_current.amp = parameters['dendrite_bias_current_amp'] # Updated in main script to play a ramped bias current through this IClamp + + # Dendrite 3: + self.d3_bias_current = h.IClamp(0.5, sec=self.d3) # IClamp used to play bias current into the dendrite + self.d3_bias_current.delay = 0 + self.d3_bias_current.dur = 1e12 + self.d3_bias_current.amp = parameters['dendrite_bias_current_amp'] # Updated in main script to play a ramped bias current through this IClamp + + # Dendrite 4: + self.d4_bias_current = h.IClamp(0.5, sec=self.d4) # IClamp used to play bias current into the dendrite + self.d4_bias_current.delay = 0 + self.d4_bias_current.dur = 1e12 + self.d4_bias_current.amp = parameters['dendrite_bias_current_amp'] # Updated in main script to play a ramped bias current through this IClamp + + # Append the gaussian IClamps to global list so they can be updated in the main script + GV.MU_dendritic_bias_current_iclamps.append([self.d1_bias_current, self.d2_bias_current, self.d3_bias_current, self.d4_bias_current]) + + # Add Double Exponential Synapses to soma and each dendritic compartment for correlated inputs from cortex + # Correlated Inputs - Excitatory Synapse: + # Soma: + self.soma_correlated_exc = h.ExpSyn(0.5, sec=self.soma) + self.soma_correlated_exc.e = parameters['correlated_e_rev_exc'] # Excitatory reversal potential + self.soma_correlated_exc.tau = parameters['correlated_tau'] # Excitatory decay time + + # Dendrite 1: + self.d1_correlated_exc = h.ExpSyn(0.5, sec=self.d1) + self.d1_correlated_exc.e = parameters['correlated_e_rev_exc'] # Excitatory reversal potential + self.d1_correlated_exc.tau = parameters['correlated_tau'] # Excitatory decay time + + # Dendrite 2: + self.d2_correlated_exc = h.ExpSyn(0.5, sec=self.d2) + self.d2_correlated_exc.e = parameters['correlated_e_rev_exc'] # Excitatory reversal potential + self.d2_correlated_exc.tau = parameters['correlated_tau'] # Excitatory decay time + + # Dendrite 3: + self.d3_correlated_exc = h.ExpSyn(0.5, sec=self.d3) + self.d3_correlated_exc.e = parameters['correlated_e_rev_exc'] # Excitatory reversal potential + self.d3_correlated_exc.tau = parameters['correlated_tau'] # Excitatory decay time + + # Dendrite 4: + self.d4_correlated_exc = h.ExpSyn(0.5, sec=self.d4) + self.d4_correlated_exc.e = parameters['correlated_e_rev_exc'] # Excitatory reversal potential + self.d4_correlated_exc.tau = parameters['correlated_tau'] # Excitatory decay time + + # Independent Inputs - Excitatory Synapse: + # Dendrite 1: + self.d1_independent_exc = h.Exp2Syn(0.5, sec=self.d1) + self.d1_independent_exc.e = parameters['independent_e_rev_exc'] # Excitatory reversal potential + self.d1_independent_exc.tau1 = parameters['independent_tau1'] # Excitatory rise time + self.d1_independent_exc.tau2 = parameters['independent_tau2'] # Excitatory decay time + + # Dendrite 2: + self.d2_independent_exc = h.Exp2Syn(0.5, sec=self.d2) + self.d2_independent_exc.e = parameters['independent_e_rev_exc'] # Excitatory reversal potential + self.d2_independent_exc.tau1 = parameters['independent_tau1'] # Excitatory rise time + self.d2_independent_exc.tau2 = parameters['independent_tau2'] # Excitatory decay time + + # Dendrite 3: + self.d3_independent_exc = h.Exp2Syn(0.5, sec=self.d3) + self.d3_independent_exc.e = parameters['independent_e_rev_exc'] # Excitatory reversal potential + self.d3_independent_exc.tau1 = parameters['independent_tau1'] # Excitatory rise time + self.d3_independent_exc.tau2 = parameters['independent_tau2'] # Excitatory decay time + + # Dendrite 4: + self.d4_independent_exc = h.Exp2Syn(0.5, sec=self.d4) + self.d4_independent_exc.e = parameters['independent_e_rev_exc'] # Excitatory reversal potential + self.d4_independent_exc.tau1 = parameters['independent_tau1'] # Excitatory rise time + self.d4_independent_exc.tau2 = parameters['independent_tau2'] # Excitatory decay time + + # Synaptic Bombardment Noise Inputs to Dendrites: + # Dendrite 1: + self.d1_Synaptic_Bombardment = h.Gfluct(0.5, sec=self.d1) + self.d1_Synaptic_Bombardment.ge0 = parameters['dendrite_Gfluct_ge0'] + self.d1_Synaptic_Bombardment.gi0 = parameters['dendrite_Gfluct_gi0'] + self.d1_Synaptic_Bombardment.stde = parameters['dendrite_Gfluct_stde'] + self.d1_Synaptic_Bombardment.stdi = parameters['dendrite_Gfluct_stdi'] + self.d1_Synaptic_Bombardment.tau_e = parameters['dendrite_Gfluct_tau_e'] + self.d1_Synaptic_Bombardment.tau_i = parameters['dendrite_Gfluct_tau_i'] + self.d1_Synaptic_Bombardment.E_e = parameters['dendrite_Gfluct_E_e'] + self.d1_Synaptic_Bombardment.E_i = parameters['dendrite_Gfluct_E_i'] + + # Set new seed for the process + d1_r = h.Random() # Make random number for seed + d1_r.Random123(GV.stream_index, 0, 0) + #self.d1_Synaptic_Bombardment.new_seed = d1_r + + # Dendrite 2: + self.d2_Synaptic_Bombardment = h.Gfluct(0.5, sec=self.d2) + self.d2_Synaptic_Bombardment.ge0 = parameters['dendrite_Gfluct_ge0'] + self.d2_Synaptic_Bombardment.gi0 = parameters['dendrite_Gfluct_gi0'] + self.d2_Synaptic_Bombardment.stde = parameters['dendrite_Gfluct_stde'] + self.d2_Synaptic_Bombardment.stdi = parameters['dendrite_Gfluct_stdi'] + self.d2_Synaptic_Bombardment.tau_e = parameters['dendrite_Gfluct_tau_e'] + self.d2_Synaptic_Bombardment.tau_i = parameters['dendrite_Gfluct_tau_i'] + self.d2_Synaptic_Bombardment.E_e = parameters['dendrite_Gfluct_E_e'] + self.d2_Synaptic_Bombardment.E_i = parameters['dendrite_Gfluct_E_i'] + + # Set new seed for the process + d2_r = h.Random() # Make random number for seed + d2_r.Random123(GV.stream_index, 1, 0) + #self.d2_Synaptic_Bombardment.new_seed = d2_r + + # Dendrite 3: + self.d3_Synaptic_Bombardment = h.Gfluct(0.5, sec=self.d3) + self.d3_Synaptic_Bombardment.ge0 = parameters['dendrite_Gfluct_ge0'] + self.d3_Synaptic_Bombardment.gi0 = parameters['dendrite_Gfluct_gi0'] + self.d3_Synaptic_Bombardment.stde = parameters['dendrite_Gfluct_stde'] + self.d3_Synaptic_Bombardment.stdi = parameters['dendrite_Gfluct_stdi'] + self.d3_Synaptic_Bombardment.tau_e = parameters['dendrite_Gfluct_tau_e'] + self.d3_Synaptic_Bombardment.tau_i = parameters['dendrite_Gfluct_tau_i'] + self.d3_Synaptic_Bombardment.E_e = parameters['dendrite_Gfluct_E_e'] + self.d3_Synaptic_Bombardment.E_i = parameters['dendrite_Gfluct_E_i'] + + # Set new seed for the process + d3_r = h.Random() # Make random number for seed + d3_r.Random123(GV.stream_index, 2, 0) + #self.d3_Synaptic_Bombardment.new_seed = d3_r + + # Dendrite 4: + self.d4_Synaptic_Bombardment = h.Gfluct(0.5, sec=self.d4) + self.d4_Synaptic_Bombardment.ge0 = parameters['dendrite_Gfluct_ge0'] + self.d4_Synaptic_Bombardment.gi0 = parameters['dendrite_Gfluct_gi0'] + self.d4_Synaptic_Bombardment.stde = parameters['dendrite_Gfluct_stde'] + self.d4_Synaptic_Bombardment.stdi = parameters['dendrite_Gfluct_stdi'] + self.d4_Synaptic_Bombardment.tau_e = parameters['dendrite_Gfluct_tau_e'] + self.d4_Synaptic_Bombardment.tau_i = parameters['dendrite_Gfluct_tau_i'] + self.d4_Synaptic_Bombardment.E_e = parameters['dendrite_Gfluct_E_e'] + self.d4_Synaptic_Bombardment.E_i = parameters['dendrite_Gfluct_E_i'] + + # Append the noisy conductances to global list so they can be updated in the main script + GV.MU_dendritic_noisy_conductances.append([self.d1_Synaptic_Bombardment, self.d2_Synaptic_Bombardment, self.d3_Synaptic_Bombardment, self.d4_Synaptic_Bombardment]) + + # Set new seed for the process + d4_r = h.Random() # Make random number for seed + d4_r.Random123(GV.stream_index, 3, 0) + #self.d4_Synaptic_Bombardment.new_seed = d4_r + + # Update the stream index for generating other random number seeds + GV.stream_index = GV.stream_index + 1 + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.source_section) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.traces = {} + self.recording_time = False + self.parameter_names = ('soma_diam', 'soma_L', 'soma_Ra', 'soma_cm', 'soma_ek', 'v_thres', + 'dendrite_diam', 'dendrite_L', 'dendrite_Ra', 'dendrite_cm', + 'soma_na3rp_gbar', 'soma_na3rp_sh', 'soma_na3rp_ar', 'soma_na3rp_qinf', 'soma_na3rp_thinf', + 'soma_naps_gbar', 'soma_naps_sh', 'soma_naps_ar', 'soma_naps_vslope', 'soma_naps_asvh', 'soma_naps_bsvh', + 'soma_kdrRL_gMax', 'soma_kdrRL_tmin', 'soma_kdrRL_taumax', 'soma_kdrRL_mVh', + 'soma_mAHP_gcamax', 'soma_mAHP_gkcamax', 'soma_mAHP_taur', 'soma_mAHP_mtauca', 'soma_mAHP_mvhalfca', + 'soma_gh_ghbar', 'soma_gh_half', 'soma_gh_htau', + 'soma_pas_g', 'soma_pas_e', + 'dendrite_pas_g', 'dendrite_pas_e', + 'dendrite_1_L_Ca_inact_gcabar', 'dendrite_2_L_Ca_inact_gcabar', 'dendrite_3_L_Ca_inact_gcabar', 'dendrite_4_L_Ca_inact_gcabar', 'dendrite_L_Ca_inact_theta_m', 'dendrite_L_Ca_inact_tau_m', 'dendrite_L_Ca_inact_theta_h', 'dendrite_L_Ca_inact_tau_h', 'dendrite_L_Ca_inact_kappa_h', + 'dendrite_gh_ghbar', 'dendrite_gh_half', 'dendrite_gh_htau', + 'correlated_e_rev_exc', 'correlated_tau', + 'independent_e_rev_exc', 'independent_tau1', 'independent_tau2', + 'soma_bias_current_amp', 'dendrite_bias_current_amp', + 'dendrite_Gfluct_ge0', 'dendrite_Gfluct_gi0', 'dendrite_Gfluct_stde', 'dendrite_Gfluct_stdi', 'dendrite_Gfluct_tau_e', 'dendrite_Gfluct_tau_i', 'dendrite_Gfluct_E_e', 'dendrite_Gfluct_E_i' + ) + self.traces = {} + self.recording_time = False + + def memb_init(self): + for seg in self.soma: # Initialize all compartment membrane voltages + seg.v = self.v_init + for seg in self.d1: + seg.v = self.v_init + for seg in self.d2: + seg.v = self.v_init + for seg in self.d3: + seg.v = self.v_init + for seg in self.d4: + seg.v = self.v_init + + def get_threshold(self): # Initialize the soma membrane threshold for netcons + return self.soma.v_thresh + + # Need to make getters and setters for changing motoneuron parameters + # Soma - + # diam Setters & Getters: + def _set_soma_diam(self, value): + self.soma.diam = value + def _get_soma_diam(self): + return self.soma.diam + soma_diam = property(fget=_get_soma_diam, fset=_set_soma_diam) + + # L Setters & Getters: + def _set_soma_L(self, value): + self.soma.L = value + def _get_soma_L(self): + return self.soma.L + soma_L = property(fget=_get_soma_L, fset=_set_soma_L) + + # cm Setters & Getters: + def _set_soma_cm(self, value): + self.soma.cm = value + def _get_soma_cm(self): + return self.soma.cm + soma_cm = property(fget=_get_soma_cm, fset=_set_soma_cm) + + # Ra Setters & Getters: + def _set_soma_Ra(self, value): + self.soma.Ra = value + def _get_soma_Ra(self): + return self.soma.Ra + soma_Ra = property(fget=_get_soma_Ra, fset=_set_soma_Ra) + + # na3rp Setters & Getters: + def _set_soma_na3rp_gbar(self, value): + for seg in self.soma: + seg.na3rp.gbar = value + def _get_soma_na3rp_gbar(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.soma(0.5).na3rp.gbar + soma_na3rp_gbar = property(fget=_get_soma_na3rp_gbar, fset=_set_soma_na3rp_gbar) + + def _set_soma_na3rp_sh(self, value): + for seg in self.soma: + seg.na3rp.sh = value + def _get_soma_na3rp_sh(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.soma(0.5).na3rp.sh + soma_na3rp_sh = property(fget=_get_soma_na3rp_sh, fset=_set_soma_na3rp_sh) + + def _set_soma_na3rp_ar(self, value): + for seg in self.soma: + seg.na3rp.ar = value + def _get_soma_na3rp_ar(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.soma(0.5).na3rp.ar + soma_na3rp_ar = property(fget=_get_soma_na3rp_ar, fset=_set_soma_na3rp_ar) + + def _set_soma_na3rp_qinf(self, value): + for seg in self.soma: + seg.na3rp.qinf = value + def _get_soma_na3rp_qinf(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.soma(0.5).na3rp.qinf + soma_na3rp_qinf = property(fget=_get_soma_na3rp_qinf, fset=_set_soma_na3rp_qinf) + + def _set_soma_na3rp_thinf(self, value): + for seg in self.soma: + seg.na3rp.thinf = value + def _get_soma_na3rp_thinf(self): + # Return only the middle soma parameter + return self.soma(0.5).na3rp.thinf + soma_na3rp_thinf = property(fget=_get_soma_na3rp_thinf, fset=_set_soma_na3rp_thinf) + + # naps Setters & Getters: + def _set_soma_naps_gbar(self, value): + for seg in self.soma: + seg.naps.gbar = value + def _get_soma_naps_gbar(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.gbar + soma_naps_gbar = property(fget=_get_soma_naps_gbar, fset=_set_soma_naps_gbar) + + def _set_soma_naps_sh(self, value): + for seg in self.soma: + seg.naps.sh = value + def _get_soma_naps_sh(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.sh + soma_naps_sh = property(fget=_get_soma_naps_sh, fset=_set_soma_naps_sh) + + def _set_soma_naps_ar(self, value): + for seg in self.soma: + seg.naps.ar = value + def _get_soma_naps_ar(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.ar + soma_naps_ar = property(fget=_get_soma_naps_ar, fset=_set_soma_naps_ar) + + def _set_soma_naps_vslope(self, value): + for seg in self.soma: + seg.naps.vslope = value + def _get_soma_naps_vslope(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.vslope + soma_naps_vslope = property(fget=_get_soma_naps_vslope, fset=_set_soma_naps_vslope) + + def _set_soma_naps_asvh(self, value): + for seg in self.soma: + seg.naps.asvh = value + def _get_soma_naps_asvh(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.asvh + soma_naps_asvh = property(fget=_get_soma_naps_asvh, fset=_set_soma_naps_asvh) + + def _set_soma_naps_bsvh(self, value): + for seg in self.soma: + seg.naps.bsvh = value + def _get_soma_naps_bsvh(self): + # Return only the middle soma parameter + return self.soma(0.5).naps.bsvh + soma_naps_bsvh = property(fget=_get_soma_naps_bsvh, fset=_set_soma_naps_bsvh) + + # kdrRL Setters & Getters: + def _set_soma_kdrRL_gMax(self, value): + for seg in self.soma: + seg.kdrRL.gMax = value + def _get_soma_kdrRL_gMax(self): + # Return only the middle soma parameter + return self.soma(0.5).kdrRL.gMax + soma_kdrRL_gMax = property(fget=_get_soma_kdrRL_gMax, fset=_set_soma_kdrRL_gMax) + + def _set_soma_kdrRL_tmin(self, value): + for seg in self.soma: + seg.kdrRL.tmin = value + def _get_soma_kdrRL_tmin(self): + # Return only the middle soma parameter + return self.soma(0.5).kdrRL.tmin + soma_kdrRL_tmin = property(fget=_get_soma_kdrRL_tmin, fset=_set_soma_kdrRL_tmin) + + def _set_soma_kdrRL_taumax(self, value): + for seg in self.soma: + seg.kdrRL.taumax = value + def _get_soma_kdrRL_taumax(self): + # Return only the middle soma parameter + return self.soma(0.5).kdrRL.taumax + soma_kdrRL_taumax = property(fget=_get_soma_kdrRL_taumax, fset=_set_soma_kdrRL_taumax) + + def _set_soma_kdrRL_mVh(self, value): + for seg in self.soma: + seg.kdrRL.mVh = value + def _get_soma_kdrRL_mVh(self): + # Return only the middle soma parameter + return self.soma(0.5).kdrRL.mVh + soma_kdrRL_mVh = property(fget=_get_soma_kdrRL_mVh, fset=_set_soma_kdrRL_mVh) + + # mAHP Setters & Getters: + def _set_soma_mAHP_gcamax(self, value): + for seg in self.soma: + seg.mAHP.gcamax = value + def _get_soma_mAHP_gcamax(self): + # Return only the middle soma parameter + return self.soma(0.5).mAHP.gcamax + soma_mAHP_gcamax = property(fget=_get_soma_mAHP_gcamax, fset=_set_soma_mAHP_gcamax) + + def _set_soma_mAHP_gkcamax(self, value): + for seg in self.soma: + seg.mAHP.gkcamax = value + def _get_soma_mAHP_gkcamax(self): + # Return only the middle soma parameter + return self.soma(0.5).mAHP.gkcamax + soma_mAHP_gkcamax = property(fget=_get_soma_mAHP_gkcamax, fset=_set_soma_mAHP_gkcamax) + + def _set_soma_mAHP_taur(self, value): + for seg in self.soma: + seg.mAHP.taur = value + def _get_soma_mAHP_taur(self): + # Return only the middle soma parameter + return self.soma(0.5).mAHP.taur + soma_mAHP_taur = property(fget=_get_soma_mAHP_taur, fset=_set_soma_mAHP_taur) + + def _set_soma_mAHP_mtauca(self, value): + for seg in self.soma: + seg.mAHP.mtauca = value + def _get_soma_mAHP_mtauca(self): + # Return only the middle soma parameter + return self.soma(0.5).mAHP.mtauca + soma_mAHP_mtauca = property(fget=_get_soma_mAHP_mtauca, fset=_set_soma_mAHP_mtauca) + + def _set_soma_mAHP_mvhalfca(self, value): + for seg in self.soma: + seg.mAHP.mvhalfca = value + def _get_soma_mAHP_mvhalfca(self): + # Return only the middle soma parameter + return self.soma(0.5).mAHP.mvhalfca + soma_mAHP_mvhalfca = property(fget=_get_soma_mAHP_mvhalfca, fset=_set_soma_mAHP_mvhalfca) + + # gh Setters & Getters: + def _set_soma_gh_ghbar(self, value): + for seg in self.soma: + seg.gh.ghbar = value + def _get_soma_gh_ghbar(self): + # Return only the middle soma parameter + return self.soma(0.5).gh.ghbar + soma_gh_ghbar = property(fget=_get_soma_gh_ghbar, fset=_set_soma_gh_ghbar) + + def _set_soma_gh_half(self, value): + for seg in self.soma: + seg.gh.half = value + def _get_soma_gh_half(self): + # Return only the middle soma parameter + return self.soma(0.5).gh.half + soma_gh_half = property(fget=_get_soma_gh_half, fset=_set_soma_gh_half) + + def _set_soma_gh_htau(self, value): + for seg in self.soma: + seg.gh.htau = value + def _get_soma_gh_htau(self): + # Return only the middle soma parameter + return self.soma(0.5).gh.htau + soma_gh_htau = property(fget=_get_soma_gh_htau, fset=_set_soma_gh_htau) + + # pas Setters & Getters: + def _set_soma_pas_g(self, value): + for seg in self.soma: + seg.pas.g = value + def _get_soma_pas_g(self): + # Return only the middle soma parameter + return self.soma(0.5).pas.g + soma_pas_g = property(fget=_get_soma_pas_g, fset=_set_soma_pas_g) + + def _set_soma_pas_e(self, value): + for seg in self.soma: + seg.pas.e = value + def _get_soma_pas_e(self): + # Return only the middle soma parameter + return self.soma(0.5).pas.e + soma_pas_e = property(fget=_get_soma_pas_e, fset=_set_soma_pas_e) + + # Dendrites - + # diam Setters & Getters: + def _set_dendrite_diam(self, value): + self.d1.diam = value + self.d2.diam = value + self.d3.diam = value + self.d4.diam = value + def _get_dendrite_diam(self): + return self.d1.diam + dendrite_diam = property(fget=_get_dendrite_diam, fset=_set_dendrite_diam) + + # L Setters & Getters: + def _set_dendrite_L(self, value): + self.d1.L = value + self.d2.L = value + self.d3.L = value + self.d4.L = value + def _get_dendrite_L(self): + return self.d1.L + dendrite_L = property(fget=_get_dendrite_L, fset=_set_dendrite_L) + + # cm Setters & Getters: + def _set_dendrite_cm(self, value): + self.d1.cm = value + self.d2.cm = value + self.d3.cm = value + self.d4.cm = value + def _get_dendrite_cm(self): + return self.d1.cm + dendrite_cm = property(fget=_get_dendrite_cm, fset=_set_dendrite_cm) + + # Ra Setters & Getters: + def _set_dendrite_Ra(self, value): + self.d1.Ra = value + self.d2.Ra = value + self.d3.Ra = value + self.d4.Ra = value + def _get_dendrite_Ra(self): + return self.d1.Ra + dendrite_Ra = property(fget=_get_dendrite_Ra, fset=_set_dendrite_Ra) + + # pas mechanism - g value: + def _set_dendrite_pas_g(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.pas.g = value + for seg in self.d2: + seg.pas.g = value + for seg in self.d3: + seg.pas.g = value + for seg in self.d4: + seg.pas.g = value + def _get_dendrite_pas_g(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).pas.g + dendrite_pas_g = property(fget=_get_dendrite_pas_g, fset=_set_dendrite_pas_g) + + # pas mechanism - e value: + def _set_dendrite_pas_e(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.pas.e = value + for seg in self.d2: + seg.pas.e = value + for seg in self.d3: + seg.pas.e = value + for seg in self.d4: + seg.pas.e = value + def _get_dendrite_pas_e(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).pas.e + dendrite_pas_e = property(fget=_get_dendrite_pas_e, fset=_set_dendrite_pas_e) + + # PIC conductance values are different on each dendrite compartment, so there are 4 getters and setters corresponding to each conductance + # L_Ca_inact mechanism - gcabar value for dendrite 1: + def _set_dendrite_1_L_Ca_inact_gcabar(self, value): + for seg in self.d1: + seg.L_Ca_inact.gcabar = value + def _get_dendrite_1_L_Ca_inact_gcabar(self): + return self.d1(0.5).L_Ca_inact.gcabar + dendrite_1_L_Ca_inact_gcabar = property(fget=_get_dendrite_1_L_Ca_inact_gcabar, fset=_set_dendrite_1_L_Ca_inact_gcabar) + + # L_Ca_inact mechanism - gcabar value for dendrite 2: + def _set_dendrite_2_L_Ca_inact_gcabar(self, value): + for seg in self.d2: + seg.L_Ca_inact.gcabar = value + def _get_dendrite_2_L_Ca_inact_gcabar(self): + return self.d2(0.5).L_Ca_inact.gcabar + dendrite_2_L_Ca_inact_gcabar = property(fget=_get_dendrite_2_L_Ca_inact_gcabar, fset=_set_dendrite_2_L_Ca_inact_gcabar) + + # L_Ca_inact mechanism - gcabar value for dendrite 3: + def _set_dendrite_3_L_Ca_inact_gcabar(self, value): + for seg in self.d3: + seg.L_Ca_inact.gcabar = value + def _get_dendrite_3_L_Ca_inact_gcabar(self): + return self.d3(0.5).L_Ca_inact.gcabar + dendrite_3_L_Ca_inact_gcabar = property(fget=_get_dendrite_3_L_Ca_inact_gcabar, fset=_set_dendrite_3_L_Ca_inact_gcabar) + + # L_Ca_inact mechanism - gcabar value for dendrite 4: + def _set_dendrite_4_L_Ca_inact_gcabar(self, value): + for seg in self.d4: + seg.L_Ca_inact.gcabar = value + def _get_dendrite_4_L_Ca_inact_gcabar(self): + return self.d4(0.5).L_Ca_inact.gcabar + dendrite_4_L_Ca_inact_gcabar = property(fget=_get_dendrite_4_L_Ca_inact_gcabar, fset=_set_dendrite_4_L_Ca_inact_gcabar) + + # L_Ca_inact mechanism - theta_m value: + def _set_dendrite_L_Ca_inact_theta_m(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.L_Ca_inact.theta_m = value + for seg in self.d2: + seg.L_Ca_inact.theta_m = value + for seg in self.d3: + seg.L_Ca_inact.theta_m = value + for seg in self.d4: + seg.L_Ca_inact.theta_m = value + def _get_dendrite_L_Ca_inact_theta_m(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).L_Ca_inact.theta_m + dendrite_L_Ca_inact_theta_m = property(fget=_get_dendrite_L_Ca_inact_theta_m, fset=_set_dendrite_L_Ca_inact_theta_m) + + # L_Ca_inact mechanism - tau_m value: + def _set_dendrite_L_Ca_inact_tau_m(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.L_Ca_inact.tau_m = value + for seg in self.d2: + seg.L_Ca_inact.tau_m = value + for seg in self.d3: + seg.L_Ca_inact.tau_m = value + for seg in self.d4: + seg.L_Ca_inact.tau_m = value + def _get_dendrite_L_Ca_inact_tau_m(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).L_Ca_inact.tau_m + dendrite_L_Ca_inact_tau_m = property(fget=_get_dendrite_L_Ca_inact_tau_m, fset=_set_dendrite_L_Ca_inact_tau_m) + + # L_Ca_inact mechanism - theta_h value: + def _set_dendrite_L_Ca_inact_theta_h(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.L_Ca_inact.theta_h = value + for seg in self.d2: + seg.L_Ca_inact.theta_h = value + for seg in self.d3: + seg.L_Ca_inact.theta_h = value + for seg in self.d4: + seg.L_Ca_inact.theta_h = value + def _get_dendrite_L_Ca_inact_theta_h(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).L_Ca_inact.theta_h + dendrite_L_Ca_inact_theta_h = property(fget=_get_dendrite_L_Ca_inact_theta_h, fset=_set_dendrite_L_Ca_inact_theta_h) + + # L_Ca_inact mechanism - tau_h value: + def _set_dendrite_L_Ca_inact_tau_h(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.L_Ca_inact.tau_h = value + for seg in self.d2: + seg.L_Ca_inact.tau_h = value + for seg in self.d3: + seg.L_Ca_inact.tau_h = value + for seg in self.d4: + seg.L_Ca_inact.tau_h = value + def _get_dendrite_L_Ca_inact_tau_h(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).L_Ca_inact.tau_h + dendrite_L_Ca_inact_tau_h = property(fget=_get_dendrite_L_Ca_inact_tau_h, fset=_set_dendrite_L_Ca_inact_tau_h) + + # L_Ca_inact mechanism - kappa_h value: + def _set_dendrite_L_Ca_inact_kappa_h(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.L_Ca_inact.kappa_h = value + for seg in self.d2: + seg.L_Ca_inact.kappa_h = value + for seg in self.d3: + seg.L_Ca_inact.kappa_h = value + for seg in self.d4: + seg.L_Ca_inact.kappa_h = value + def _get_dendrite_L_Ca_inact_kappa_h(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).L_Ca_inact.kappa_h + dendrite_L_Ca_inact_kappa_h = property(fget=_get_dendrite_L_Ca_inact_kappa_h, fset=_set_dendrite_L_Ca_inact_kappa_h) + + # gh mechanism - ghbar value: + def _set_dendrite_gh_ghbar(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.gh.ghbar = value + for seg in self.d2: + seg.gh.ghbar = value + for seg in self.d3: + seg.gh.ghbar = value + for seg in self.d4: + seg.gh.ghbar = value + def _get_dendrite_gh_ghbar(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).gh.ghbar + dendrite_gh_ghbar = property(fget=_get_dendrite_gh_ghbar, fset=_set_dendrite_gh_ghbar) + + # gh mechanism - half value: + def _set_dendrite_gh_half(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.gh.half = value + for seg in self.d2: + seg.gh.half = value + for seg in self.d3: + seg.gh.half = value + for seg in self.d4: + seg.gh.half = value + def _get_dendrite_gh_half(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).gh.half + dendrite_gh_half = property(fget=_get_dendrite_gh_half, fset=_set_dendrite_gh_half) + + # gh mechanism - htau value: + def _set_dendrite_gh_htau(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + for seg in self.d1: + seg.gh.htau = value + for seg in self.d2: + seg.gh.htau = value + for seg in self.d3: + seg.gh.htau = value + for seg in self.d4: + seg.gh.htau = value + def _get_dendrite_gh_htau(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1(0.5).gh.htau + dendrite_gh_htau = property(fget=_get_dendrite_gh_htau, fset=_set_dendrite_gh_htau) + + # Getters and Setters for dendritic synapses + # Correlated Inputs - Excitatory Inputs: + def _get_correlated_e_rev_exc(self): # Reversal Potential + return self.d1_correlated_exc.e + def _set_correlated_e_rev_exc(self, value): + self.soma_correlated_exc.e = value + self.d1_correlated_exc.e = value + self.d2_correlated_exc.e = value + self.d3_correlated_exc.e = value + self.d4_correlated_exc.e = value + correlated_e_rev_exc = property(fget=_get_correlated_e_rev_exc, fset=_set_correlated_e_rev_exc) + + def _get_correlated_tau(self): # Decay time (ms) + return self.d1_correlated_exc.tau + def _set_correlated_tau(self, value): + self.soma_correlated_exc.tau = value + self.d1_correlated_exc.tau = value + self.d2_correlated_exc.tau = value + self.d3_correlated_exc.tau = value + self.d4_correlated_exc.tau = value + correlated_tau = property(fget=_get_correlated_tau, fset=_set_correlated_tau) + + # Independent Inputs - Excitatory Inputs: + def _get_independent_e_rev_exc(self): # Reversal Potential + return self.d1_independent_exc.e + def _set_independent_e_rev_exc(self, value): + self.d1_independent_exc.e = value + self.d2_independent_exc.e = value + self.d3_independent_exc.e = value + self.d4_independent_exc.e = value + independent_e_rev_exc = property(fget=_get_independent_e_rev_exc, fset=_set_independent_e_rev_exc) + + def _get_independent_tau1(self): # Rise time (ms) + return self.d1_independent_exc.tau1 + def _set_independent_tau1(self, value): + # Excitatory Independent Inputs + self.d1_independent_exc.tau1 = value + self.d2_independent_exc.tau1 = value + self.d3_independent_exc.tau1 = value + self.d4_independent_exc.tau1 = value + independent_tau1 = property(fget=_get_independent_tau1, fset=_set_independent_tau1) + + def _get_independent_tau2(self): # Decay time (ms) + return self.d1_independent_exc.tau2 + def _set_independent_tau2(self, value): + # Excitatory Independent Inputs + self.d1_independent_exc.tau2 = value + self.d2_independent_exc.tau2 = value + self.d3_independent_exc.tau2 = value + self.d4_independent_exc.tau2 = value + independent_tau2 = property(fget=_get_independent_tau2, fset=_set_independent_tau2) + + def _get_soma_bias_current_amp(self): # Getters/Setters for the soma bias current (nA) + return self.soma_bias.amp + def _set_soma_bias_current_amp(self, value): + self.soma_stim.bias = value + soma_bias_current_amp = property(fget=_get_soma_bias_current_amp, fset=_set_soma_bias_current_amp) + + def _get_dendrite_bias_current_amp(self): # Getters/Setters for the dendrite bias current (nA) + return self.d1_bias_current.amp + def _set_dendrite_bias_current_amp(self, value): + self.d1_bias_current.amp = value + self.d2_bias_current.amp = value + self.d3_bias_current.amp = value + self.d4_bias_current.amp = value + dendrite_bias_current_amp = property(fget=_get_dendrite_bias_current_amp, fset=_set_dendrite_bias_current_amp) + + # Getters and Setters for dendritic stochastic conductance - simulates synaptic bombardment from other structures + # Gfluct mechanism - ge0 value: + def _set_dendrite_Gfluct_g_e0(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.ge0 = value + self.d2_Synaptic_Bombardment.ge0 = value + self.d3_Synaptic_Bombardment.ge0 = value + self.d4_Synaptic_Bombardment.ge0 = value + + def _get_dendrite_Gfluct_g_e0(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.ge0 + dendrite_Gfluct_ge0 = property(fget=_get_dendrite_Gfluct_g_e0, fset=_set_dendrite_Gfluct_g_e0) + + # Gfluct mechanism - gi0 value: + def _set_dendrite_Gfluct_g_i0(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.gi0 = value + self.d2_Synaptic_Bombardment.gi0 = value + self.d3_Synaptic_Bombardment.gi0 = value + self.d4_Synaptic_Bombardment.gi0 = value + + def _get_dendrite_Gfluct_g_i0(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.gi0 + dendrite_Gfluct_gi0 = property(fget=_get_dendrite_Gfluct_g_i0, fset=_set_dendrite_Gfluct_g_i0) + + # Gfluct mechanism - stde value: + def _set_dendrite_Gfluct_std_e(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.stde = value + self.d2_Synaptic_Bombardment.stde = value + self.d3_Synaptic_Bombardment.stde = value + self.d4_Synaptic_Bombardment.stde = value + + def _get_dendrite_Gfluct_std_e(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.stde + dendrite_Gfluct_stde = property(fget=_get_dendrite_Gfluct_std_e, fset=_set_dendrite_Gfluct_std_e) + + # Gfluct mechanism - stdi value: + def _set_dendrite_Gfluct_std_i(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.stdi = value + self.d2_Synaptic_Bombardment.stdi = value + self.d3_Synaptic_Bombardment.stdi = value + self.d4_Synaptic_Bombardment.stdi = value + + def _get_dendrite_Gfluct_std_i(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.stdi + dendrite_Gfluct_stdi = property(fget=_get_dendrite_Gfluct_std_i, fset=_set_dendrite_Gfluct_std_i) + + # Gfluct mechanism - tau_e value: + def _set_dendrite_Gfluct_tau_e(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.tau_e = value + self.d2_Synaptic_Bombardment.tau_e = value + self.d3_Synaptic_Bombardment.tau_e = value + self.d4_Synaptic_Bombardment.tau_e = value + + def _get_dendrite_Gfluct_tau_e(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.tau_e + dendrite_Gfluct_tau_e = property(fget=_get_dendrite_Gfluct_tau_e, fset=_set_dendrite_Gfluct_tau_e) + + # Gfluct mechanism - tau_i value: + def _set_dendrite_Gfluct_tau_i(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.tau_i = value + self.d2_Synaptic_Bombardment.tau_i = value + self.d3_Synaptic_Bombardment.tau_i = value + self.d4_Synaptic_Bombardment.tau_i = value + + def _get_dendrite_Gfluct_tau_i(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.tau_i + dendrite_Gfluct_tau_i = property(fget=_get_dendrite_Gfluct_tau_i, fset=_set_dendrite_Gfluct_tau_i) + + # Gfluct mechanism - E_e value: + def _set_dendrite_Gfluct_E_e(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.E_e = value + self.d2_Synaptic_Bombardment.E_e = value + self.d3_Synaptic_Bombardment.E_e = value + self.d4_Synaptic_Bombardment.E_e = value + + def _get_dendrite_Gfluct_E_e(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.E_e + dendrite_Gfluct_E_e = property(fget=_get_dendrite_Gfluct_E_e, fset=_set_dendrite_Gfluct_E_e) + + # Gfluct mechanism - E_i value: + def _set_dendrite_Gfluct_E_i(self, value): + # Set each dendrite on the motoneuron to have the same parameter value + self.d1_Synaptic_Bombardment.E_i = value + self.d2_Synaptic_Bombardment.E_i = value + self.d3_Synaptic_Bombardment.E_i = value + self.d4_Synaptic_Bombardment.E_i = value + + def _get_dendrite_Gfluct_E_i(self): + # Return only the first dendrite parameter, since other dendrites use same value + return self.d1_Synaptic_Bombardment.E_i + dendrite_Gfluct_E_i = property(fget=_get_dendrite_Gfluct_E_i, fset=_set_dendrite_Gfluct_E_i) + +class Spinal_Motoneuron_Synaptic_Bombardment_Point_Process_Type(NativeCellType): + + # Default parameters for the neurons: + default_parameters = {'soma_diam': 22.0, 'soma_L': 2952.0, 'soma_Ra': 0.001, 'soma_cm': 1.35546, 'soma_ek': -80.0, 'v_thres' : -20, + 'dendrite_diam': 8.73071, 'dendrite_L': 1794.13, 'dendrite_Ra': 51.038, 'dendrite_cm': 0.867781} + # Soma default parameters: + # na3rp: + default_parameters['soma_na3rp_gbar'] = 0.01 + default_parameters['soma_na3rp_sh'] = 1.0 + default_parameters['soma_na3rp_ar'] = 1.0 + default_parameters['soma_na3rp_qinf'] = 8.0 + default_parameters['soma_na3rp_thinf'] = -50.0 + + # naps: + default_parameters['soma_naps_gbar'] = 2.6e-5 + default_parameters['soma_naps_sh'] = 5.0 + default_parameters['soma_naps_ar'] = 1.0 + default_parameters['soma_naps_vslope'] = 5.0 + default_parameters['soma_naps_asvh'] = -90.0 + default_parameters['soma_naps_bsvh'] = -22.0 + + # kdrRL: + default_parameters['soma_kdrRL_gMax'] = 0.015 + default_parameters['soma_kdrRL_tmin'] = 0.8 + default_parameters['soma_kdrRL_taumax'] = 20.0 + default_parameters['soma_kdrRL_mVh'] = -21.0 + + # mAHP: + default_parameters['soma_mAHP_gcamax'] = 6.4e-06 + default_parameters['soma_mAHP_gkcamax'] = 0.00045 + default_parameters['soma_mAHP_taur'] = 90.0 + default_parameters['soma_mAHP_mtauca'] = 2.0 + default_parameters['soma_mAHP_mvhalfca'] = -20.0 + + # gh: + default_parameters['soma_gh_ghbar'] = 3e-5 + default_parameters['soma_gh_half'] = -77.0 + default_parameters['soma_gh_htau'] = 30.0 + + # pas: + default_parameters['soma_pas_g'] = 8.11e-05 + default_parameters['soma_pas_e'] = -71.0 + + # Dendrite default parameters: + # pas: + default_parameters['dendrite_pas_g'] = 7.93e-05 + default_parameters['dendrite_pas_e'] = -71.0 + + # L_Ca_inact: + default_parameters['dendrite_1_L_Ca_inact_gcabar'] = 5.2e-6 + default_parameters['dendrite_2_L_Ca_inact_gcabar'] = 4.25e-6 + default_parameters['dendrite_3_L_Ca_inact_gcabar'] = 6.62e-6 + default_parameters['dendrite_4_L_Ca_inact_gcabar'] = 8.03e-6 + default_parameters['dendrite_L_Ca_inact_theta_m'] = -42.0 + default_parameters['dendrite_L_Ca_inact_tau_m'] = 40.0 + default_parameters['dendrite_L_Ca_inact_theta_h'] = 10.0 + default_parameters['dendrite_L_Ca_inact_tau_h'] = 2500.0 + default_parameters['dendrite_L_Ca_inact_kappa_h'] = 5.0 + + # gh: + default_parameters['dendrite_gh_ghbar'] = 3e-5 + default_parameters['dendrite_gh_half'] = -77.0 + default_parameters['dendrite_gh_htau'] = 30.0 + + # Dendritic synaptic parameters - + # Correlated Input Parameters: + default_parameters['correlated_e_rev_exc'] = 0.0 + default_parameters['correlated_tau'] = 25.0 + + # Independent Input Parameters: + default_parameters['independent_e_rev_exc'] = 0.0 + default_parameters['independent_tau1'] = 4.0 + default_parameters['independent_tau2'] = 20.0 + + # Bias Current Values + default_parameters['soma_bias_current_amp'] = 0.0 + default_parameters['dendrite_bias_current_amp'] = 0.0 + + # Stochastic conductance parameters to simulate inputs from other structures + default_parameters['dendrite_Gfluct_ge0'] = 0.15 # mean value of excitatory conductance - representing input from other neural structures + default_parameters['dendrite_Gfluct_stde'] = 0.25 # standard deviation of excitatory conductance - representing input from other neural structures + default_parameters['dendrite_Gfluct_gi0'] = 0.3 # mean value of inhibitory conductance - representing input from other neural structures + default_parameters['dendrite_Gfluct_stdi'] = 0.25 # standard deviation of inhibitory conductance - representing input from other neural structures + default_parameters['dendrite_Gfluct_tau_e'] = 2.728 # Excitatory Current Time constant - In Powers et al. 2012 Paper value is 2.728 ms to simulate uncorrelated/independent inputs + default_parameters['dendrite_Gfluct_tau_i'] = 10.49 # Inhibitory Current Time constant - In Powers et al. 2012 Paper value is 10.49 ms to simulate uncorrelated/independent inputs + default_parameters['dendrite_Gfluct_E_e'] = 0.0 + default_parameters['dendrite_Gfluct_E_i'] = -75.0 + + default_initial_values = {'v': -65.0} # Can record soma voltage and synaptic currents on each dendrite + recordable = ['soma(0.5).v', 'd1(0.5).v', 'd2(0.5).v', 'd3(0.5).v', 'd4(0.5).v', 'soma_correlated_exc.i', 'd1_correlated_exc.i', 'd2_correlated_exc.i', 'd3_correlated_exc.i', 'd4_correlated_exc.i', 'd1_independent_exc.i', 'd2_independent_exc.i', 'd3_independent_exc.i', 'd4_independent_exc.i', 'd1_Synaptic_Bombardment.i', 'd2_Synaptic_Bombardment.i', 'd3_Synaptic_Bombardment.i', 'd4_Synaptic_Bombardment.i', 'd1_Synaptic_Bombardment.g_e', 'd2_Synaptic_Bombardment.g_e', 'd3_Synaptic_Bombardment.g_e', 'd4_Synaptic_Bombardment.g_e', 'd1_Synaptic_Bombardment.g_i', 'd2_Synaptic_Bombardment.g_i', 'd3_Synaptic_Bombardment.g_i', 'd4_Synaptic_Bombardment.g_i'] + units = {'soma(0.5).v' : 'mV', 'd1(0.5).v' : 'mV', 'd2(0.5).v' : 'mV', 'd3(0.5).v' : 'mV', 'd4(0.5).v' : 'mV', 'soma_correlated_exc.i' : 'nA', 'd1_correlated_exc.i' : 'nA', 'd2_correlated_exc.i' : 'nA', 'd3_correlated_exc.i' : 'nA', 'd4_correlated_exc.i' : 'nA', 'd1_independent_exc.i' : 'nA', 'd2_independent_exc.i' : 'nA', 'd3_independent_exc.i' : 'nA', 'd4_independent_exc.i' : 'nA', 'd1_Synaptic_Bombardment.i' : 'nA', 'd2_Synaptic_Bombardment.i' : 'nA', 'd3_Synaptic_Bombardment.i' : 'nA', 'd4_Synaptic_Bombardment.i' : 'nA', 'd1_Synaptic_Bombardment.g_e' : 'uS', 'd2_Synaptic_Bombardment.g_e' : 'uS', 'd3_Synaptic_Bombardment.g_e' : 'uS', 'd4_Synaptic_Bombardment.g_e' : 'uS', 'd1_Synaptic_Bombardment.g_i' : 'uS', 'd2_Synaptic_Bombardment.g_i' : 'uS', 'd3_Synaptic_Bombardment.g_i' : 'uS', 'd4_Synaptic_Bombardment.g_i' : 'uS'} + receptor_types = ['soma_correlated_exc', 'd1_correlated_exc', 'd2_correlated_exc', 'd3_correlated_exc', 'd4_correlated_exc', 'd1_independent_exc', 'd2_independent_exc', 'd3_independent_exc', 'd4_independent_exc'] + model = Spinal_Motoneuron_Synaptic_Bombardment_Point_Process + +class Spinal_Interneuron(object): + + def __init__(self, **parameters): + + # Create single compartment Destexhe Interneuron cell section, i.e. soma section + self.soma = Section(L=parameters['L'], diam=parameters['diam'], nseg=parameters['nseg'], Ra=parameters['Ra'], cm=parameters['cm'], + mechanisms=()) + #mechanisms=(Mechanism('hh'))) + #mechanisms=(Mechanism('interneuron_i_leak'), Mechanism('interneuron_i_na'), Mechanism('interneuron_i_k'))) + + # Insert the basic HH mechanisms to the soma + self.soma.insert('hh') + + # Set the default mechanism parameters + for seg in self.soma: + seg.hh.gnabar = 120e-3 + seg.hh.gkbar = 100e-3 + seg.hh.gl = 0.51e-3 + seg.hh.el = -64 + + # Add bias current to neuron model - current amplitude is in terms of original model paper, nA + self.soma_bias_current = h.IClamp(0.5, sec=self.soma) + self.soma_bias_current.delay = 0 + self.soma_bias_current.dur = 1e12 + self.soma_bias_current.amp = parameters['bias_current_amp'] # nA + + # Append the gaussian IClamps to global list so they can be updated in the main script + GV.Spinal_Interneuron_bias_current_iclamps.append(self.soma_bias_current) + + # Add excitatory synapse to the cell, i.e. add to the soma section + self.synapse_exc = h.ExpSyn(0.5, sec=self.soma) + self.synapse_exc.e = parameters['e_rev'] + self.synapse_exc.tau = parameters['tau'] + + # needed for PyNN + self.source_section = self.soma + self.source = self.soma(0.5)._ref_v + self.rec = h.NetCon(self.source, None, sec=self.source_section) # Needed to clear the simulator + self.spike_times = h.Vector(0) + self.parameter_names = ('L', 'diam', 'nseg', 'Ra', 'cm', 'bias_current_amp', 'e_rev', 'tau') + self.traces = {} + self.recording_time = False + + L = _new_property('soma', 'L') + diam = _new_property('soma', 'diam') + nseg = _new_property('soma', 'nseg') + Ra = _new_property('soma', 'Ra') + cm = _new_property('soma', 'cm') + bias_current_amp = _new_property('soma_bias_current', 'amp') + + def area(self): + """Membrane area in µm²""" + return pi * self.soma.L * self.soma.diam + + def memb_init(self): + for seg in self.soma: + seg.v = self.v_init + +class Spinal_Interneuron_Type(NativeCellType): + default_parameters = {'L': 100, 'diam': 500, 'nseg': 1, 'Ra': 150, 'cm': 1, 'bias_current_amp': 0, 'e_rev': -10, 'tau': 5} + default_initial_values = {'v': -68.0} + recordable = ['soma(0.5).v', 'synapse_exc.i'] + units = {'soma(0.5).v' : 'mV', 'synapse_exc.i' : 'nA'} + receptor_types = ['synapse_exc'] + model = Spinal_Interneuron + +# Function to generate a skewed parameter values for a single parameter value +def generate_skewed_parameter_values(Pop_size, lower_parameter_bound, upper_parameter_bound, nonlin_ind): + + # Set Up Nonlinear Interpolation Function - skew + nonlin_ind = motoneuron_pool_parameter_bounds['nonlin_ind'] + fraction = np.zeros(Pop_size) + for cell_index in range(Pop_size): + fraction[cell_index] = float(cell_index)/(Pop_size - 1) + if nonlin_ind >= 0: + skew = fraction**(1 + nonlin_ind) + else: + skew = 1 - (1-fraction)**(1 - nonlin_ind) + + # Calculate the skewed parameter + parameter_values = lower_parameter_bound + (upper_parameter_bound - lower_parameter_bound)*skew + + # return the skewed parameter values + return parameter_values + +# Function to generate the distribution of parameter values across the motoneurons in the pool +def generate_motoneuron_population_parameters(Pop_size, motoneuron_pool_parameter_bounds): + + # Create dictionary for holding the parameters + motoneuron_parameters = {} + + # Set Up Nonlinear Interpolation Function - skew + nonlin_ind = motoneuron_pool_parameter_bounds['nonlin_ind'] + fraction = np.zeros(Pop_size) + for cell_index in range(Pop_size): + fraction[cell_index] = float(cell_index)/(Pop_size - 1) + if nonlin_ind >= 0: + skew = fraction**(1 + nonlin_ind) + else: + skew = 1 - (1-fraction)**(1 - nonlin_ind) + + # Get the motoneuron sections to update parameters for + motoneuron_sections = list(motoneuron_pool_parameter_bounds.keys()) + + # Increment over the sections + for i in np.arange(1,len(motoneuron_sections)): # Skip the first skew parameter + section_parameters = list(motoneuron_pool_parameter_bounds[motoneuron_sections[i]].keys()) + # Increment over the parameters in the section + for j in np.arange(0,len(section_parameters)): + section_subparameters = list(motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]].keys()) + # Checking/setting cell geometry parameters + if section_subparameters == ['lower', 'upper']: + parameter_key = motoneuron_sections[i]+"_"+section_parameters[j] + lower_parameter_bound = motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]]['lower'] + upper_parameter_bound = motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]]['upper'] + + # Calculate the skewed parameters + parameter_values = lower_parameter_bound + (upper_parameter_bound - lower_parameter_bound)*skew + + # Add the parameter values to the parameter dictionary + motoneuron_parameters[parameter_key] = parameter_values + + else: + for k in np.arange(0,len(section_subparameters)): + mechanism_parameters = list(motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]][section_subparameters[k]].keys()) + parameter_key = motoneuron_sections[i]+"_"+section_parameters[j]+"_"+section_subparameters[k] + lower_parameter_bound = motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]][section_subparameters[k]]['lower'] + upper_parameter_bound = motoneuron_pool_parameter_bounds[motoneuron_sections[i]][section_parameters[j]][section_subparameters[k]]['upper'] + + # Calculate the skewed parameters + parameter_values = lower_parameter_bound + (upper_parameter_bound - lower_parameter_bound)*skew + + # Add the parameter values to the parameter dictionary + motoneuron_parameters[parameter_key] = parameter_values + + # Return the updated motoneuron population + return motoneuron_parameters + +# Motoneuron parameter bounds for the pool +motoneuron_pool_parameter_bounds = { + "nonlin_ind" : 0.8, + "soma" : { + "diam" : {"lower" : 22, "upper" : 30}, + "L" : {"lower" : 2952, "upper" : 3665}, + "cm" : {"lower" : 1.35546, "upper" : 1.87853}, + "pas" : { + "g" : {"lower" : 8.11e-05, "upper" : 3.77e-04}, + "e" : {"lower" : -71, "upper" : -72} + }, + "na3rp" : { + "gbar" : {"lower" : 0.01, "upper" : 0.022}, + "sh" : {"lower" : 1.0, "upper" : 1.0}, + "ar" : {"lower" : 1.0, "upper" : 1.0}, + "qinf" : {"lower" : 8.0, "upper" : 8.0}, + "thinf" : {"lower" : -50.0, "upper" : -50.0} + }, + "naps" : { + "gbar" : {"lower" : 2.60e-05, "upper" : 2.00e-05}, + "sh" : {"lower" : 5.0, "upper" : 5.0}, + "ar" : {"lower" : 1.0, "upper" : 1.0}, + "vslope" : {"lower" : 5.0, "upper" : 5.0}, + "asvh" : {"lower" : -90.0, "upper" : -90.0}, + "bsvh" : {"lower" : -22.0, "upper" : -22.0} + }, + "kdrRL" : { + "gMax" : {"lower" : 0.015, "upper" : 0.02}, + "mVh" : {"lower" : -21, "upper" : -21}, + "tmin" : {"lower" : 0.8, "upper" : 0.8}, + "taumax" : {"lower" : 20.0, "upper" : 20.0} + }, + "mAHP" : { + "gcamax" : {"lower" : 6.40e-06, "upper" : 1.05e-05}, + "gkcamax" : {"lower" : 0.00045, "upper" : 0.0006}, + "taur" : {"lower" : 90, "upper" : 40}, + "mvhalfca" : {"lower" : -20, "upper" : -20}, + "mtauca" : {"lower" : 2, "upper" : 2} + }, + "gh" : { + "ghbar" : {"lower" : 3.00e-05, "upper" : 2.30e-04}, + "htau" : {"lower" : 30, "upper" : 30} + }, + }, + "dendrite" : { + "diam" : {"lower" : 8.73071, "upper" : 11.9055}, + "L" : {"lower" : 1794.13, "upper" : 2226.91}, + "Ra" : {"lower" : 51.038, "upper" : 40.755}, + "cm" : {"lower" : 0.867781, "upper" : 0.880407}, + "pas" : { + "g" : {"lower" : 7.93e-05, "upper" : 1.75e-04}, + "e" : {"lower" : -71, "upper" : -72} + }, + "gh" : { + "ghbar" : {"lower" : 3.00e-05, "upper" : 2.30e-04}, + "htau" : {"lower" : 30, "upper" : 30} + }, + "L_Ca_inact" : { + "theta_m" : {"lower" : -42, "upper" : -32}, + "tau_m" : {"lower" : 40, "upper" : 40}, + "theta_h" : {"lower" : 10, "upper" : -10}, + "tau_h" : {"lower" : 2500, "upper" : 2500}, + "kappa_h" : {"lower" : 5, "upper" : 5} + } + }, + "dendrite_1" : { + "L_Ca_inact" : { + "gcabar" : {"lower" : 5.20e-06, "upper" : 5.44e-06} + } + }, + "dendrite_2" : { + "L_Ca_inact" : { + "gcabar" : {"lower" : 4.25e-06, "upper" : 6.24e-06} + } + }, + "dendrite_3" : { + "L_Ca_inact" : { + "gcabar" : {"lower" : 6.62e-06, "upper" : 7.04e-06} + } + }, + "dendrite_4" : { + "L_Ca_inact" : { + "gcabar" : {"lower" : 8.03e-06, "upper" : 8.24e-06} + } + } +} \ No newline at end of file diff --git a/Destexhe_Static_AMPA_Synapse.mod b/Destexhe_Static_AMPA_Synapse.mod new file mode 100644 index 0000000..bfdf5d7 --- /dev/null +++ b/Destexhe_Static_AMPA_Synapse.mod @@ -0,0 +1,202 @@ +TITLE simple AMPA receptors (discrete connections) + +COMMENT +----------------------------------------------------------------------------- + + Simple model for glutamate AMPA receptors + ========================================= + + - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS + + Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate + receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used + to estimate the parameters of the present model; the fit was performed + using a simplex algorithm (see Destexhe et al., J. Computational Neurosci. + 1: 195-230, 1994). + + - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM) + + The simplified model was obtained from a detailed synaptic model that + included the release of transmitter in adjacent terminals, its lateral + diffusion and uptake, and its binding on postsynaptic receptors (Destexhe + and Sejnowski, 1995). Short pulses of transmitter with first-order + kinetics were found to be the best fast alternative to represent the more + detailed models. + + - ANALYTIC EXPRESSION + + The first-order model can be solved analytically, leading to a very fast + mechanism for simulating synapses, since no differential equation must be + solved (see references below). + + + +References + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. An efficient method for + computing synaptic conductances based on a kinetic model of receptor binding + Neural Computation 6: 10-14, 1994. + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for + excitable membranes, synaptic transmission and neuromodulation using a + common kinetic formalism, Journal of Computational Neuroscience 1: + 195-230, 1994. + +See also: + + http://www.cnl.salk.edu/~alain + http://cns.fmed.ulaval.ca + +----------------------------------------------------------------------------- +ENDCOMMENT + + + +:INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + POINT_PROCESS AMPA_S + RANGE C, R, R0, R1, g, Cmax + NONSPECIFIC_CURRENT i + :GLOBAL Cdur, Alpha, Beta, Erev, blockTime, Rinf, Rtau +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (umho) = (micromho) + (mM) = (milli/liter) +} + +PARAMETER { + + Cmax = 0.5 (mM) : max transmitter concentration (set = 1 to match ~/netcon/ampa.hoc) + Cdur = 0.3 (ms) : transmitter duration (rising phase) + Alpha = 0.94 (/ms mM) : forward (binding) rate + Beta = 0.18 (/ms) : backward (unbinding) rate + Erev = 0 (mV) : reversal potential + blockTime = 4 (ms) : time window following dbs event during which non-dbs events are blocked +} + + +ASSIGNED { + v (mV) : postsynaptic voltage + i (nA) : current = g*(v - Erev) + g (umho) : conductance + C (mM) : transmitter concentration + R : fraction of open channels + R0 : open channels at start of time period + Rinf : steady state channels open + Rtau (ms) : time constant of channel binding + on : rising phase of PSC + gmax : max conductance + tLast + nspike + collisionBlock +} + +INITIAL { + R = 0 + C = 0 + Rinf = Cmax*Alpha / (Cmax*Alpha + Beta) + Rtau = 1 / ((Alpha * Cmax) + Beta) + on = 0 + R0 = 0 + nspike = 0 + collisionBlock = 0 +} + +BREAKPOINT { + SOLVE release + i = R*(v - Erev) +} + +PROCEDURE release() { + + + if (on) { : transmitter being released? + + R = gmax*Rinf + (R0 - gmax*Rinf) * exptable (- (t - tLast) / Rtau) + + } else { : no release occuring + + R = R0 * exptable (- Beta * (t - tLast)) + } + +} + + +: following supports both saturation from single input and +: summation from multiple inputs +: if spike occurs during CDur then new off time is t + CDur +: ie. transmitter concatenates but does not summate +: Note: automatic initialization of all reference args to 0 except first + +NET_RECEIVE(weight, ncType, ncPrb) {LOCAL ok, tmp + + :ncType 0=presyn cell, 1=dbs activated axon + :MOVED TO dbsStim.mod 4/11/07 ncPrb probability that incoming event causes PSP + + INITIAL { + } + + : flag is an implicit argument of NET_RECEIVE and normally 0 + if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse + ok = 0 + + if (ncType == 1) { + collisionBlock = collisionBlock + 1 + net_send(blockTime, -1) + +: tmp = scop_random() +: if (tmp <= ncPrb) { +: ok = 1 +: } + ok = 1 + + } + else + if (collisionBlock == 0) { + ok = 1 + } + + if (ok) { + if (!on) { + on = 1 + tLast = t + R0 = R + gmax = weight :weight not additive from separate sources as in original ampa.mod + } + + nspike = nspike + 1 + : come again in Cdur with flag = current value of nspike + net_send(Cdur, nspike) + } + } + else + if (flag == nspike) { : if this associated with last spike then turn off + if (on) { + on = 0 + tLast = t + R0 = R + gmax = 0 + } + } + else + if (flag == -1) { + collisionBlock = collisionBlock - 1 + } + + +} + + +FUNCTION exptable(x) { + TABLE FROM -10 TO 10 WITH 2000 + + if ((x > -10) && (x < 10)) { + exptable = exp(x) + } else { + exptable = 0. + } +} diff --git a/Destexhe_Static_GABAA_Synapse.mod b/Destexhe_Static_GABAA_Synapse.mod new file mode 100644 index 0000000..66dcfcc --- /dev/null +++ b/Destexhe_Static_GABAA_Synapse.mod @@ -0,0 +1,202 @@ +TITLE simple GABA-A receptors, based on AMPA model (discrete connections) + +COMMENT +----------------------------------------------------------------------------- + + Simple model for glutamate AMPA receptors + ========================================= + + - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS + + Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate + receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used + to estimate the parameters of the present model; the fit was performed + using a simplex algorithm (see Destexhe et al., J. Computational Neurosci. + 1: 195-230, 1994). + + - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM) + + The simplified model was obtained from a detailed synaptic model that + included the release of transmitter in adjacent terminals, its lateral + diffusion and uptake, and its binding on postsynaptic receptors (Destexhe + and Sejnowski, 1995). Short pulses of transmitter with first-order + kinetics were found to be the best fast alternative to represent the more + detailed models. + + - ANALYTIC EXPRESSION + + The first-order model can be solved analytically, leading to a very fast + mechanism for simulating synapses, since no differential equation must be + solved (see references below). + + + +References + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. An efficient method for + computing synaptic conductances based on a kinetic model of receptor binding + Neural Computation 6: 10-14, 1994. + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for + excitable membranes, synaptic transmission and neuromodulation using a + common kinetic formalism, Journal of Computational Neuroscience 1: + 195-230, 1994. + +See also: + + http://www.cnl.salk.edu/~alain + http://cns.fmed.ulaval.ca + +----------------------------------------------------------------------------- +ENDCOMMENT + + + +:INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + POINT_PROCESS GABAa_S + RANGE C, R, R0, R1, g, Cmax + NONSPECIFIC_CURRENT i + :GLOBAL Cdur, Alpha, Beta, Erev, blockTime, Rinf, Rtau +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (umho) = (micromho) + (mM) = (milli/liter) +} + +PARAMETER { + + Cmax = 0.5 (mM) : max transmitter concentration (set = 1 to match ~/netcon/ampa.hoc) + Cdur = 0.3 (ms) : transmitter duration (rising phase) + Alpha = 10.5 (/ms mM) : forward (binding) rate + Beta = 0.166 (/ms) : backward (unbinding) rate + Erev = -80 (mV) : reversal potential + blockTime = 2 (ms) : time window following dbs event during which non-dbs events are blocked +} + + +ASSIGNED { + v (mV) : postsynaptic voltage + i (nA) : current = g*(v - Erev) + g (umho) : conductance + C (mM) : transmitter concentration + R : fraction of open channels + R0 : open channels at start of time period + Rinf : steady state channels open + Rtau (ms) : time constant of channel binding + on : rising phase of PSC + gmax : max conductance + tLast + nspike + collisionBlock +} + +INITIAL { + R = 0 + C = 0 + Rinf = Cmax*Alpha / (Cmax*Alpha + Beta) + Rtau = 1 / ((Alpha * Cmax) + Beta) + on = 0 + R0 = 0 + nspike = 0 + collisionBlock = 0 +} + +BREAKPOINT { + SOLVE release + i = R*(v - Erev) +} + +PROCEDURE release() { + + + if (on) { : transmitter being released? + + R = gmax*Rinf + (R0 - gmax*Rinf) * exptable (- (t - tLast) / Rtau) + + } else { : no release occuring + + R = R0 * exptable (- Beta * (t - tLast)) + } + +} + + +: following supports both saturation from single input and +: summation from multiple inputs +: if spike occurs during CDur then new off time is t + CDur +: ie. transmitter concatenates but does not summate +: Note: automatic initialization of all reference args to 0 except first + +NET_RECEIVE(weight, ncType, ncPrb) {LOCAL ok, tmp + + :ncType 0=presyn cell, 1=dbs activated axon + :MOVED TO dbsStim.mod 4/11/07 ncPrb probability that incoming event causes PSP + + INITIAL { + } + + : flag is an implicit argument of NET_RECEIVE and normally 0 + if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse + ok = 0 + + if (ncType == 1) { + collisionBlock = collisionBlock + 1 + net_send(blockTime, -1) + +: tmp = scop_random() +: if (tmp <= ncPrb) { +: ok = 1 +: } + ok = 1 + + } + else + if (collisionBlock == 0) { + ok = 1 + } + + if (ok) { + if (!on) { + on = 1 + tLast = t + R0 = R + gmax = weight :weight not additive from separate sources as in original ampa.mod + } + + nspike = nspike + 1 + : come again in Cdur with flag = current value of nspike + net_send(Cdur, nspike) + } + } + else + if (flag == nspike) { : if this associated with last spike then turn off + if (on) { + on = 0 + tLast = t + R0 = R + gmax = 0 + } + } + else + if (flag == -1) { + collisionBlock = collisionBlock - 1 + } + + +} + + +FUNCTION exptable(x) { + TABLE FROM -10 TO 10 WITH 2000 + + if ((x > -10) && (x < 10)) { + exptable = exp(x) + } else { + exptable = 0. + } +} diff --git a/Electrode_Distances.py b/Electrode_Distances.py new file mode 100644 index 0000000..cdcd35e --- /dev/null +++ b/Electrode_Distances.py @@ -0,0 +1,101 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed April 03 14:27:26 2019 + +Description: Functions for calculating cell distances to electrode. Distance is + calculated similar to space.distance, but distance is now between + a cell position and a point for an electrode rather than between two cell positions + +Edits: + 10-01-18: Created electrode_distance function + +@author: John Fleming, john.fleming@ucdconnect.ie + +""" + +# There must be some Python package out there that provides most of this stuff. +# Distance computations are provided by scipy.spatial, but scipy is a fairly heavy dependency. + +try: + reduce +except NameError: + from functools import reduce +import numpy +import math +from operator import and_ +from pyNN.random import NumpyRNG +from pyNN import descriptions +import logging + +logger = logging.getLogger("PyNN") + +def distance_to_electrode(src_electrode, tgt_cell, mask=None): + """ + Return the Euclidian distance from a point source electrode to a cell. + `mask` allows only certain dimensions to be considered, e.g.:: + * to ignore the z-dimension, use `mask=array([0,1])` + * to ignore y, `mask=array([0,2])` + * to just consider z-distance, `mask=array([2])` + 'src_electrode' is the electrode positon in xyz co-ordinates. + 'tgt_cell' is the cell that the distance will be calculated to. + """ + d = src_electrode - tgt_cell.position + + if mask is not None: + d = d[mask] + return numpy.sqrt(numpy.dot(d, d)) + +def distances_to_electrode(src_electrode, tgt_pop, coordinate_mask=None): + """ + Return an array of the Euclidian distances from a point source + electrode to a population of cells. + `coordinate_mask` allows only certain dimensions to be considered, e.g.:: + * to ignore the z-dimension, use `coordinate_mask=array([0,1])` + * to ignore y, `coordinate_mask=array([0,2])` + * to just consider z-distance, `coordinate_mask=array([2])` + 'src_electrode' is the electrode positon in xyz co-ordinates. + 'tgt_pop' is the population of cells that the distance will be calculated to. + """ + row_size,col_size = tgt_pop.positions.shape + cell_electrode_distances = numpy.zeros((col_size,1)) + cell_electrode_distances.flatten() + + for cell_id, tgt_cell in enumerate(tgt_pop): + cell_electrode_distances[cell_id] = distance_to_electrode(src_electrode,tgt_cell,mask=coordinate_mask) + + return cell_electrode_distances + #return cell_electrode_distances.flatten() + +def collateral_distances_to_electrode(src_electrode, tgt_pop, L, nseg): + """ + Return an nd-array of the Euclidian distances from a point source + electrode to a population of cells. Each row corresponds to a collateral + from the cortical population. Each column corresponds to the segments of the + collateral, with 0 being the furthest segment from the 2d plane the cells are + distributed in and 1 being in the plane. + 'src_electrode' is the electrode positon in xyz co-ordinates. + 'tgt_pop' is the population of cells that the distance will be calculated to. + 'L' is the length of the cortical collateral + 'nseg' is the number of segments in a collateral + 'segment_electrode_distances' is the distance from the centre of each collateral + segment to the stimulating electrode. Each row corresponds to a collateral of a single + cortical cell. Each column corresponds to a segment of the collateral. + """ + row_size,col_size = tgt_pop.positions.shape + segment_electrode_distances = numpy.zeros((col_size,nseg)) + + segment_centres = numpy.arange(0, nseg+3-1)*(1/nseg) + segment_centres = segment_centres - (1/(2*nseg)) + segment_centres[0] = 0 + segment_centres[-1] = 1 + segment_centres = segment_centres[1:len(segment_centres)-1] + + z_coordinate = L*segment_centres - L/2 + #print(z_coordinate) + + for cell_id, tgt_cell in enumerate(tgt_pop): + for seg in numpy.arange(nseg): + tgt_cell.position = numpy.array([tgt_cell.position[0], tgt_cell.position[1], z_coordinate[seg]]) + segment_electrode_distances[cell_id][seg] = distance_to_electrode(src_electrode,tgt_cell) + + return segment_electrode_distances \ No newline at end of file diff --git a/GPeGPe_Connections.txt b/GPeGPe_Connections.txt new file mode 100644 index 0000000..f0cd5b1 --- /dev/null +++ b/GPeGPe_Connections.txt @@ -0,0 +1,101 @@ +# columns = ['i', 'j', 'weight', 'delay'] +42.0 0.0 0.015 4.0 +13.0 1.0 0.015 4.0 +70.0 2.0 0.015 4.0 +26.0 3.0 0.015 4.0 +95.0 4.0 0.015 4.0 +7.0 5.0 0.015 4.0 +12.0 6.0 0.015 4.0 +4.0 7.0 0.015 4.0 +82.0 8.0 0.015 4.0 +27.0 9.0 0.015 4.0 +24.0 10.0 0.015 4.0 +15.0 11.0 0.015 4.0 +30.0 12.0 0.015 4.0 +90.0 13.0 0.015 4.0 +60.0 14.0 0.015 4.0 +7.0 15.0 0.015 4.0 +35.0 16.0 0.015 4.0 +34.0 17.0 0.015 4.0 +10.0 18.0 0.015 4.0 +12.0 19.0 0.015 4.0 +65.0 20.0 0.015 4.0 +86.0 21.0 0.015 4.0 +62.0 22.0 0.015 4.0 +22.0 23.0 0.015 4.0 +40.0 24.0 0.015 4.0 +41.0 25.0 0.015 4.0 +41.0 26.0 0.015 4.0 +6.0 27.0 0.015 4.0 +10.0 28.0 0.015 4.0 +60.0 29.0 0.015 4.0 +1.0 30.0 0.015 4.0 +57.0 31.0 0.015 4.0 +72.0 32.0 0.015 4.0 +15.0 33.0 0.015 4.0 +40.0 34.0 0.015 4.0 +74.0 35.0 0.015 4.0 +33.0 36.0 0.015 4.0 +71.0 37.0 0.015 4.0 +75.0 38.0 0.015 4.0 +56.0 39.0 0.015 4.0 +5.0 40.0 0.015 4.0 +81.0 41.0 0.015 4.0 +73.0 42.0 0.015 4.0 +40.0 43.0 0.015 4.0 +99.0 44.0 0.015 4.0 +3.0 45.0 0.015 4.0 +89.0 46.0 0.015 4.0 +71.0 47.0 0.015 4.0 +3.0 48.0 0.015 4.0 +61.0 49.0 0.015 4.0 +13.0 50.0 0.015 4.0 +54.0 51.0 0.015 4.0 +49.0 52.0 0.015 4.0 +62.0 53.0 0.015 4.0 +94.0 54.0 0.015 4.0 +84.0 55.0 0.015 4.0 +54.0 56.0 0.015 4.0 +70.0 57.0 0.015 4.0 +21.0 58.0 0.015 4.0 +53.0 59.0 0.015 4.0 +59.0 60.0 0.015 4.0 +57.0 61.0 0.015 4.0 +97.0 62.0 0.015 4.0 +95.0 63.0 0.015 4.0 +30.0 64.0 0.015 4.0 +44.0 65.0 0.015 4.0 +2.0 66.0 0.015 4.0 +37.0 67.0 0.015 4.0 +35.0 68.0 0.015 4.0 +21.0 69.0 0.015 4.0 +75.0 70.0 0.015 4.0 +87.0 71.0 0.015 4.0 +23.0 72.0 0.015 4.0 +15.0 73.0 0.015 4.0 +44.0 74.0 0.015 4.0 +19.0 75.0 0.015 4.0 +12.0 76.0 0.015 4.0 +0.0 77.0 0.015 4.0 +62.0 78.0 0.015 4.0 +57.0 79.0 0.015 4.0 +55.0 80.0 0.015 4.0 +90.0 81.0 0.015 4.0 +29.0 82.0 0.015 4.0 +17.0 83.0 0.015 4.0 +74.0 84.0 0.015 4.0 +78.0 85.0 0.015 4.0 +54.0 86.0 0.015 4.0 +20.0 87.0 0.015 4.0 +8.0 88.0 0.015 4.0 +24.0 89.0 0.015 4.0 +48.0 90.0 0.015 4.0 +97.0 91.0 0.015 4.0 +79.0 92.0 0.015 4.0 +76.0 93.0 0.015 4.0 +97.0 94.0 0.015 4.0 +21.0 95.0 0.015 4.0 +71.0 96.0 0.015 4.0 +64.0 97.0 0.015 4.0 +84.0 98.0 0.015 4.0 +86.0 99.0 0.015 4.0 diff --git a/GPeGPi_Connections.txt b/GPeGPi_Connections.txt new file mode 100644 index 0000000..67cd77f --- /dev/null +++ b/GPeGPi_Connections.txt @@ -0,0 +1,101 @@ +# columns = ['i', 'j', 'weight', 'delay'] +41.0 0.0 0.111111 2.0 +12.0 1.0 0.111111 2.0 +56.0 2.0 0.111111 2.0 +25.0 3.0 0.111111 2.0 +95.0 4.0 0.111111 2.0 +6.0 5.0 0.111111 2.0 +5.0 6.0 0.111111 2.0 +4.0 7.0 0.111111 2.0 +81.0 8.0 0.111111 2.0 +26.0 9.0 0.111111 2.0 +55.0 10.0 0.111111 2.0 +14.0 11.0 0.111111 2.0 +29.0 12.0 0.111111 2.0 +90.0 13.0 0.111111 2.0 +59.0 14.0 0.111111 2.0 +7.0 15.0 0.111111 2.0 +32.0 16.0 0.111111 2.0 +0.0 17.0 0.111111 2.0 +10.0 18.0 0.111111 2.0 +12.0 19.0 0.111111 2.0 +64.0 20.0 0.111111 2.0 +17.0 21.0 0.111111 2.0 +61.0 22.0 0.111111 2.0 +22.0 23.0 0.111111 2.0 +44.0 24.0 0.111111 2.0 +2.0 25.0 0.111111 2.0 +41.0 26.0 0.111111 2.0 +26.0 27.0 0.111111 2.0 +32.0 28.0 0.111111 2.0 +82.0 29.0 0.111111 2.0 +66.0 30.0 0.111111 2.0 +9.0 31.0 0.111111 2.0 +85.0 32.0 0.111111 2.0 +19.0 33.0 0.111111 2.0 +99.0 34.0 0.111111 2.0 +47.0 35.0 0.111111 2.0 +35.0 36.0 0.111111 2.0 +48.0 37.0 0.111111 2.0 +48.0 38.0 0.111111 2.0 +85.0 39.0 0.111111 2.0 +10.0 40.0 0.111111 2.0 +80.0 41.0 0.111111 2.0 +43.0 42.0 0.111111 2.0 +63.0 43.0 0.111111 2.0 +35.0 44.0 0.111111 2.0 +41.0 45.0 0.111111 2.0 +70.0 46.0 0.111111 2.0 +3.0 47.0 0.111111 2.0 +80.0 48.0 0.111111 2.0 +13.0 49.0 0.111111 2.0 +61.0 50.0 0.111111 2.0 +84.0 51.0 0.111111 2.0 +27.0 52.0 0.111111 2.0 +9.0 53.0 0.111111 2.0 +32.0 54.0 0.111111 2.0 +50.0 55.0 0.111111 2.0 +3.0 56.0 0.111111 2.0 +10.0 57.0 0.111111 2.0 +48.0 58.0 0.111111 2.0 +70.0 59.0 0.111111 2.0 +19.0 60.0 0.111111 2.0 +93.0 61.0 0.111111 2.0 +87.0 62.0 0.111111 2.0 +57.0 63.0 0.111111 2.0 +91.0 64.0 0.111111 2.0 +31.0 65.0 0.111111 2.0 +41.0 66.0 0.111111 2.0 +64.0 67.0 0.111111 2.0 +67.0 68.0 0.111111 2.0 +22.0 69.0 0.111111 2.0 +44.0 70.0 0.111111 2.0 +33.0 71.0 0.111111 2.0 +13.0 72.0 0.111111 2.0 +44.0 73.0 0.111111 2.0 +82.0 74.0 0.111111 2.0 +12.0 75.0 0.111111 2.0 +0.0 76.0 0.111111 2.0 +63.0 77.0 0.111111 2.0 +57.0 78.0 0.111111 2.0 +55.0 79.0 0.111111 2.0 +58.0 80.0 0.111111 2.0 +64.0 81.0 0.111111 2.0 +19.0 82.0 0.111111 2.0 +38.0 83.0 0.111111 2.0 +12.0 84.0 0.111111 2.0 +6.0 85.0 0.111111 2.0 +1.0 86.0 0.111111 2.0 +81.0 87.0 0.111111 2.0 +58.0 88.0 0.111111 2.0 +77.0 89.0 0.111111 2.0 +48.0 90.0 0.111111 2.0 +82.0 91.0 0.111111 2.0 +37.0 92.0 0.111111 2.0 +4.0 93.0 0.111111 2.0 +20.0 94.0 0.111111 2.0 +39.0 95.0 0.111111 2.0 +72.0 96.0 0.111111 2.0 +85.0 97.0 0.111111 2.0 +87.0 98.0 0.111111 2.0 +61.0 99.0 0.111111 2.0 diff --git a/GPeSTN_Connections.txt b/GPeSTN_Connections.txt new file mode 100644 index 0000000..224557a --- /dev/null +++ b/GPeSTN_Connections.txt @@ -0,0 +1,201 @@ +# columns = ['i', 'j', 'weight', 'delay'] +41.0 0.0 0.111111 3.0 +60.0 0.0 0.111111 3.0 +12.0 1.0 0.111111 3.0 +11.0 1.0 0.111111 3.0 +56.0 2.0 0.111111 3.0 +84.0 2.0 0.111111 3.0 +25.0 3.0 0.111111 3.0 +14.0 3.0 0.111111 3.0 +95.0 4.0 0.111111 3.0 +72.0 4.0 0.111111 3.0 +6.0 5.0 0.111111 3.0 +26.0 5.0 0.111111 3.0 +5.0 6.0 0.111111 3.0 +47.0 6.0 0.111111 3.0 +4.0 7.0 0.111111 3.0 +82.0 7.0 0.111111 3.0 +81.0 8.0 0.111111 3.0 +12.0 8.0 0.111111 3.0 +26.0 9.0 0.111111 3.0 +56.0 9.0 0.111111 3.0 +55.0 10.0 0.111111 3.0 +35.0 10.0 0.111111 3.0 +14.0 11.0 0.111111 3.0 +28.0 11.0 0.111111 3.0 +29.0 12.0 0.111111 3.0 +3.0 12.0 0.111111 3.0 +90.0 13.0 0.111111 3.0 +58.0 13.0 0.111111 3.0 +59.0 14.0 0.111111 3.0 +0.0 14.0 0.111111 3.0 +7.0 15.0 0.111111 3.0 +58.0 15.0 0.111111 3.0 +32.0 16.0 0.111111 3.0 +43.0 16.0 0.111111 3.0 +0.0 17.0 0.111111 3.0 +27.0 17.0 0.111111 3.0 +10.0 18.0 0.111111 3.0 +59.0 18.0 0.111111 3.0 +12.0 19.0 0.111111 3.0 +50.0 19.0 0.111111 3.0 +64.0 20.0 0.111111 3.0 +34.0 20.0 0.111111 3.0 +17.0 21.0 0.111111 3.0 +38.0 21.0 0.111111 3.0 +61.0 22.0 0.111111 3.0 +19.0 22.0 0.111111 3.0 +22.0 23.0 0.111111 3.0 +73.0 23.0 0.111111 3.0 +44.0 24.0 0.111111 3.0 +7.0 24.0 0.111111 3.0 +2.0 25.0 0.111111 3.0 +28.0 25.0 0.111111 3.0 +41.0 26.0 0.111111 3.0 +71.0 26.0 0.111111 3.0 +26.0 27.0 0.111111 3.0 +60.0 27.0 0.111111 3.0 +32.0 28.0 0.111111 3.0 +25.0 28.0 0.111111 3.0 +82.0 29.0 0.111111 3.0 +13.0 29.0 0.111111 3.0 +66.0 30.0 0.111111 3.0 +71.0 30.0 0.111111 3.0 +9.0 31.0 0.111111 3.0 +25.0 31.0 0.111111 3.0 +85.0 32.0 0.111111 3.0 +58.0 32.0 0.111111 3.0 +19.0 33.0 0.111111 3.0 +24.0 33.0 0.111111 3.0 +99.0 34.0 0.111111 3.0 +28.0 34.0 0.111111 3.0 +47.0 35.0 0.111111 3.0 +3.0 35.0 0.111111 3.0 +35.0 36.0 0.111111 3.0 +48.0 36.0 0.111111 3.0 +48.0 37.0 0.111111 3.0 +11.0 37.0 0.111111 3.0 +48.0 38.0 0.111111 3.0 +25.0 38.0 0.111111 3.0 +85.0 39.0 0.111111 3.0 +10.0 39.0 0.111111 3.0 +10.0 40.0 0.111111 3.0 +74.0 40.0 0.111111 3.0 +80.0 41.0 0.111111 3.0 +73.0 41.0 0.111111 3.0 +43.0 42.0 0.111111 3.0 +66.0 42.0 0.111111 3.0 +63.0 43.0 0.111111 3.0 +75.0 43.0 0.111111 3.0 +35.0 44.0 0.111111 3.0 +68.0 44.0 0.111111 3.0 +41.0 45.0 0.111111 3.0 +3.0 45.0 0.111111 3.0 +70.0 46.0 0.111111 3.0 +88.0 46.0 0.111111 3.0 +3.0 47.0 0.111111 3.0 +15.0 47.0 0.111111 3.0 +80.0 48.0 0.111111 3.0 +13.0 48.0 0.111111 3.0 +13.0 49.0 0.111111 3.0 +54.0 49.0 0.111111 3.0 +61.0 50.0 0.111111 3.0 +77.0 50.0 0.111111 3.0 +84.0 51.0 0.111111 3.0 +12.0 51.0 0.111111 3.0 +27.0 52.0 0.111111 3.0 +64.0 52.0 0.111111 3.0 +9.0 53.0 0.111111 3.0 +88.0 53.0 0.111111 3.0 +32.0 54.0 0.111111 3.0 +9.0 54.0 0.111111 3.0 +50.0 55.0 0.111111 3.0 +98.0 55.0 0.111111 3.0 +3.0 56.0 0.111111 3.0 +73.0 56.0 0.111111 3.0 +10.0 57.0 0.111111 3.0 +32.0 57.0 0.111111 3.0 +48.0 58.0 0.111111 3.0 +24.0 58.0 0.111111 3.0 +70.0 59.0 0.111111 3.0 +20.0 59.0 0.111111 3.0 +19.0 60.0 0.111111 3.0 +28.0 60.0 0.111111 3.0 +93.0 61.0 0.111111 3.0 +87.0 61.0 0.111111 3.0 +87.0 62.0 0.111111 3.0 +22.0 62.0 0.111111 3.0 +57.0 63.0 0.111111 3.0 +40.0 63.0 0.111111 3.0 +91.0 64.0 0.111111 3.0 +66.0 64.0 0.111111 3.0 +31.0 65.0 0.111111 3.0 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model containing + two fluctuating conductances g_e(t) and g_i(t) descibed by: + + Isyn = g_e(t) * [V - E_e] + g_i(t) * [V - E_i] + d g_e / dt = -(g_e - ge0) / tau_e + sqrt(D_e) * Ft + d g_i / dt = -(g_i - gi0) / tau_i + sqrt(D_i) * Ft + + where E_e, E_i are the reversal potentials, ge0, gi0 are the average + conductances, tau_e, tau_i are time constants, D_e, D_i are noise diffusion + coefficients and Ft is a gaussian white noise of unit standard deviation. + + g_e and g_i are described by an Ornstein-Uhlenbeck (OU) stochastic process + where tau_e and tau_i represent the "correlation" (if tau_e and tau_i are + zero, g_e and g_i are white noise). The estimation of OU parameters can + be made from the power spectrum: + + S(w) = 2 * D * tau^2 / (1 + w^2 * tau^2) + + and the diffusion coeffient D is estimated from the variance: + + D = 2 * sigma^2 / tau + + +NUMERICAL RESOLUTION + + The numerical scheme for integration of OU processes takes advantage + of the fact that these processes are gaussian, which led to an exact + update rule independent of the time step dt (see Gillespie DT, Am J Phys + 64: 225, 1996): + + x(t+dt) = x(t) * exp(-dt/tau) + A * N(0,1) + + where A = sqrt( D*tau/2 * (1-exp(-2*dt/tau)) ) and N(0,1) is a normal + random number (avg=0, sigma=1) + + +IMPLEMENTATION + + This mechanism is implemented as a nonspecific current defined as a + point process. + + +PARAMETERS + + The mechanism takes the following parameters: + + E_e = 0 (mV) : reversal potential of excitatory conductance + E_i = -75 (mV) : reversal potential of inhibitory conductance + + ge0 = 0.0121 (umho) : average excitatory conductance + gi0 = 0.0573 (umho) : average inhibitory conductance + + stde = 0.0030 (umho) : standard dev of excitatory conductance + stdi = 0.0066 (umho) : standard dev of inhibitory conductance + + tau_e = 2.728 (ms) : time constant of excitatory conductance + tau_i = 10.49 (ms) : time constant of inhibitory conductance + +REFERENCE + + Destexhe, A., Rudolph, M., Fellous, J-M. and Sejnowski, T.J. + Fluctuating synaptic conductances recreate in-vivo--like activity in + neocortical neurons. Neuroscience 107: 13-24 (2001). + + (electronic copy available at http://cns.iaf.cnrs-gif.fr) + + A. Destexhe, 1999 + +----------------------------------------------------------------------------- + +20150414 -- Ted Carnevale +Fixed so that zero value for tau_e or tau_i results in +"white noise" fluctuation of g_e or g_i. +In the previous implementation, tau_e or tau_i == 0 +made g_e or g_i equal to ge0 or gi0. + +The fix involved +1. restructuring conditional statements so that +zero value for tau_e or tau_i had desired effect, +and +2. moving all calls to normrand from the BREAKPOINT block +into PROCEDURE oup. + +The latter was necessary because code in the BREAKPOINT block +is executed twice per time step in order to estimate di/dv +(the slope conductance of this "channel") for the Jacobian matrix. +The estimate is simply +di/dv ~ (i(v+0.001) - i(v))/0.001 +where the current i is calculated by a statement of the form +i = f(v) +and f() is an algebraic expression that involves v. + +Clearly if the algebraic expression involves terms that change +from one call to the next, the estimate of di/dv will be incorrect, +and the solution will be corrupted. +The most obvious symptom of this is an occasional, abrupt, large +jump of v, but even step-to-step small fluctuations of v will be +incorrect. + +The solution is to relegate calls to random number generators +either to a PROCEDURE that is SOLVEd, or to a BEFORE BREAKPOINT block, +because code in those blocks is executed only once per advance. +ENDCOMMENT + +NEURON { + POINT_PROCESS Gfluct + RANGE g_e, g_i, E_e, E_i, ge0, gi0, g_e1, g_i1 + RANGE stde, stdi, tau_e, tau_i, D_e, D_i + RANGE new_seed + NONSPECIFIC_CURRENT i +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (umho) = (micromho) +} + +PARAMETER { + dt (ms) + + E_e = 0 (mV) : reversal potential of excitatory conductance + E_i = -75 (mV) : reversal potential of inhibitory conductance + + ge0 = 0.0121 (umho) : average excitatory conductance + gi0 = 0.0573 (umho) : average inhibitory conductance + + stde = 0.0030 (umho) : standard dev of excitatory conductance + stdi = 0.0066 (umho) : standard dev of inhibitory conductance + + tau_e = 2.728 (ms) : time constant of excitatory conductance + tau_i = 10.49 (ms) : time constant of inhibitory conductance +} + +ASSIGNED { + v (mV) : membrane voltage + i (nA) : fluctuating current + g_e (umho) : total excitatory conductance + g_i (umho) : total inhibitory conductance + g_e1 (umho) : fluctuating excitatory conductance + g_i1 (umho) : fluctuating inhibitory conductance + D_e (umho umho /ms) : excitatory diffusion coefficient + D_i (umho umho /ms) : inhibitory diffusion coefficient + exp_e + exp_i + amp_e (umho) + amp_i (umho) +} + +INITIAL { + g_e1 = 0 + g_i1 = 0 + if(tau_e != 0) { + D_e = 2 * stde * stde / tau_e + exp_e = exp(-dt/tau_e) + amp_e = stde * sqrt( (1-exp(-2*dt/tau_e)) ) + } + if(tau_i != 0) { + D_i = 2 * stdi * stdi / tau_i + exp_i = exp(-dt/tau_i) + amp_i = stdi * sqrt( (1-exp(-2*dt/tau_i)) ) + } +} + +BREAKPOINT { + SOLVE oup + i = g_e * (v - E_e) + g_i * (v - E_i) +} + +PROCEDURE oup() { : use Scop function normrand(mean, std_dev) + if(tau_e==0) { + g_e = ge0 + stde * normrand(0,1) + } else { + g_e1 = exp_e * g_e1 + amp_e * normrand(0,1) + g_e = ge0 + g_e1 + } + if (g_e < 0) { g_e = 0 } + if(tau_i==0) { + g_i = gi0 + stdi * normrand(0,1) + } else { + g_i1 = exp_i * g_i1 + amp_i * normrand(0,1) + g_i = gi0 + g_i1 + } + if (g_i < 0) { g_i = 0 } +} + +PROCEDURE new_seed(seed) { : procedure to set the seed + set_seed(seed) + VERBATIM + printf("Setting random generator with seed = %g\n", _lseed); + ENDVERBATIM +} \ No newline at end of file diff --git a/Global_Variables.py b/Global_Variables.py new file mode 100644 index 0000000..01e6910 --- /dev/null +++ b/Global_Variables.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed April 03 14:27:26 2019 + +Description: Define arrays for holding global references to the GPe DBS + stimulation signals. + + Paper Ref . . . +. + + +@author: John Fleming, john.fleming@ucdconnect.ie +""" + +# Define DBS GPe signal globally +GPe_DBS_Signals = [] +GPe_stimulation_iclamps = [] + +# Define global list of MU dendritic bias current signals +MU_dendritic_bias_current_iclamps = [] + +# Define global list of noise currents for spinal interneurons +Spinal_Interneuron_bias_current_iclamps = [] + +# Define global list of MU dendritic noisy conductances +MU_dendritic_noisy_conductances = [] + +# List to append the random streams to (so won't disappear after initialization) for MU independent input currents on dendrites +rslist = [] + +# Variable to hold the offset of the random streams +random_stream_offset = 0 + +# Index variable for generating the independent random stream for each neuron +stream_index = 0 \ No newline at end of file diff --git a/Independent_Beta_Spike_Times.npy b/Independent_Beta_Spike_Times.npy new file mode 100644 index 0000000000000000000000000000000000000000..d0a89748d1687e961f4c56c7428469797363db75 GIT binary patch literal 4543399 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