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spillover_experiment.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Sep 1 16:36:50 2017
@author: daniel
"""
from neuron import h
import experiment as e
import parameters as p
import time
import random as rnd
import numpy as np
import sys
import matplotlib.pyplot as plt
import seaborn as sns
# ex.insert_synapses('noise_SPN')
# ex.insert_synapses('my_spillover', plateau_cluster_list, deterministic = 0,
# num_syns = num_syns, add_spine = add_spine, on_spine = on_spine)
# ex.set_up_recording(dend_record_list)
class Spillover_Experiment(e.Experiment):
def __init__(self, exptype, cell, dendstatobj = [], training_mode = p.training_mode):
super(Spillover_Experiment, self).__init__()
# self.exptype = exptype
# self.cell = cell
# self.dendstatobj = dendstatobj
# self.training_mode = training_mode
# self.spike_flags = []
# self.exglusec = []
# self.exglu = []
# self.exnc = []
# if (str(type(cell))).find('MSN') != -1:
# self.celltype = 'MSN'
# elif (str(type(cell))).find('L5PC') != -1:
# self.celltype = 'L5PC'
# self.insert_synapses('L5PC')
def insert_synapses(self, syntype, syn_loc = [], deterministic = 0,
num_syns = p.distributed_input_size, add_spine = 0, on_spine = 0):
if syntype in ['expsyn', 'inhexpsyn']:
if syntype in ['expsyn']:
num_syns = p.distributed_input_size
elif syntype == 'inhexpsyn':
num_syns = p.inhibitory_cluster_size
if syn_loc == []:
for i in range (0,num_syns):
syn_loc.append(rnd.randint(0,len(self.cell.dendlist)-1))
for loc in syn_loc:
if syntype == 'expsyn_hom':
self.cell.dendlist[loc].insert('Hom_Cai')
syn = self.cell.insert_synapse(syntype, self.cell.dendlist[loc], p.pos, add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn, syntype)
elif syntype == 'input_syn':
syn_loc = []
for i in range(0, num_syns):
syn_loc.append([rnd.randint(0, len(self.cell.dendlist)-1), rnd.uniform(0,1)])
if deterministic == 1:
spike_time = []
for i in range(0, len(syn_loc)):
spike_time.append(rnd.uniform(p.distributed_input_start, p.distributed_input_end))
counter = 0
for loc in syn_loc:
counter += 1
syn1 = self.cell.insert_synapse('AMPA', self.cell.dendlist[loc[0]], loc[1],
add_spine = 0, on_spine = 0)
syn2 = self.cell.insert_synapse('NMDA', self.cell.dendlist[loc[0]], loc[1],
add_spine = 0, on_spine = 0)
# syn = self.cell.insert_synapse('glutamate', self.cell.dendlist[loc[0]], loc[1], add_spine = add_spine, on_spine = on_spine)
if deterministic == 1:
# self.add_input_generator(syn, syntype, deterministic = deterministic, numsyn = counter, tstart = spike_time[counter-1])
self.add_input_generator(syn1, 'AMPA', deterministic = deterministic, numsyn = counter, tstart = spike_time[counter-1])
self.connect_input_generator(syn2, 'NMDA', syn1.stim[-1])
else:
# self.add_input_generator(syn, syntype, deterministic = deterministic, numsyn = counter)
self.add_input_generator(syn1, 'AMPA', deterministic = deterministic, numsyn = counter)
self.connect_input_generator(syn2, 'NMDA', syn1.stim[-1])
elif syntype == 'ramp':
for loc in syn_loc:
syn = self.cell.insert_synapse('expsyn',self.cell.dendlist[loc], p.pos, add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn, syntype)
elif syntype == 'noise_SPN':
for dend in self.cell.dendlist:
# Synapses according to Cheng et al. Experimental Neurobiology, 147:287-298 (1997)
unit_length = 20.0 # Values reported in units per 20 microns in the study
[exc_mean, exc_sem, inh_mean, inh_sem] = self.synapse_distribution(self.celltype, dend)
# Insert excitatory synapses in this section,
# This contains both an exponential and an NMDA synapse.
syntype = 'glutamate'
if dend.nseg <= exc_mean:
freq_multiplier = exc_mean/dend.nseg
step = 1/dend.nseg
for i in range(0, dend.nseg):
pos = (i + (i+1))*step/2
self.helper_insert(syntype, pos, dend, freq_multiplier)
else:
num_exc_syn = int(dend.L/unit_length * rnd.gauss(exc_mean,exc_sem))
freq_multiplier = 1.0
for i in range(0,num_exc_syn):
pos = rnd.uniform(0,1)
self.helper_insert(syntype, pos, dend, freq_multiplier)
# Insert inhibitory synapses in this section
syntype = 'inhexp2syn'
if dend.nseg <= inh_mean:
freq_multiplier = inh_mean/dend.nseg
step = 1/dend.nseg
for i in range(0, dend.nseg):
pos = (i + (i+1))*step/2
self.helper_insert(syntype, pos, dend, freq_multiplier)
else:
num_inh_syn = int(dend.L/unit_length * rnd.gauss(inh_mean,inh_sem))
freq_multiplier = 1.0
for i in range(0,num_inh_syn):
pos = rnd.uniform(0,1)
self.helper_insert(syntype, pos, dend, freq_multiplier)
elif syntype in ['plateau_cluster', 'inhexpsyn_plateau', 'generalized_rule',
'spillover', 'spillover_test', 'no_spillover', 'my_spillover',
'no_spillover_stp', 'my_spillover_stp']:
if syntype == 'plateau_cluster':
syntype = 'tmGlut'
elif syntype == 'spillover':
syntype = 'adaptive_glutamate_test'
elif syntype == 'my_spillover' or syntype == 'my_spillover_stp':
self.exglusec.append(h.Section(name = 'exglusec%d' % len(self.exglusec)))
self.exglu.append(h.IntFire1(self.exglusec[-1](0.5)))
self.exglu[-1].tau = p.exglu_tau
self.exglu[-1].refrac = p.session_length
# h.setpointer(h._ref_stimulus_flag, 'stimulus_flag', self.exglusec[-1].exglu)
for num,loc in enumerate(syn_loc):
syn_step = 1.0/num_syns
cluster_start_pos = p.cluster_start_poss[p.input_dends.index(loc)]
cluster_end_pos = p.cluster_end_poss[p.input_dends.index(loc)]
for i in range(0, num_syns):
# pos = cluster_start_pos + (cluster_end_pos - cluster_start_pos)*i*syn_step
pos = cluster_end_pos - (cluster_end_pos - cluster_start_pos)*i*syn_step
if syntype == 'inhexpsyn_plateau':
if deterministic == 0:
start = rnd.uniform(p.inhibitory_burst_start, p.inhibitory_burst_end)
elif deterministic == 1:
start = p.inhibitory_burst_start + i*p.deterministic_interval
pos = p.inh_cluster_start_pos + (p.inh_cluster_end_pos - p.inh_cluster_start_pos)*i*syn_step
else:
if deterministic == 0:
start = rnd.uniform(p.plateau_burst_start, p.plateau_burst_end)
elif deterministic == 1:
start = p.plateau_burst_start + i*p.deterministic_interval
if not (syntype in ['spillover_test', 'no_spillover', 'my_spillover', 'no_spillover_stp',
'my_spillover_stp']):
syn = self.cell.insert_synapse(syntype, self.cell.dendlist[loc],
pos, add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn, syntype, deterministic = deterministic, numsyn = i, tstart = start)
if syntype == 'generalized_rule':
h.setpointer(h._ref_dopamine, 'dopamine', syn.obj)
h.setpointer(h._ref_stimulus_flag, 'stimulus_flag', syn.obj)
elif syntype == 'adaptive_glutamate_test':
h.setpointer(h._ref_dopamine, 'dopamine', syn.obj)
elif syntype in ['spillover_test', 'my_spillover']:
syn1 = self.cell.insert_synapse('AMPA', self.cell.dendlist[loc], pos,
add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn1, 'AMPA', deterministic = deterministic, numsyn = i, tstart = start)
if self.cell.spines != []:
spines = [s for s in self.cell.spines if s.parent == self.cell.dendlist[loc]]
spines[i].syn_on = 0
syn2 = self.cell.insert_synapse('NMDA', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 1)
self.connect_input_generator(syn2, 'NMDA', syn1.stim[-1])
syn3 = self.cell.insert_synapse('NMDAe', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 0)
syn1.clustered_flag = syn2.clustered_flag = syn3.clustered_flag = True
if syntype == 'spillover_test':
self.connect_input_generator(syn3, 'NMDAe', syn2.stim[-1], delay = p.delay_exnmda)
elif syntype == 'my_spillover':
self.connect_input_generator(self.exglu[-1], 't_exglu', syn1.stim[-1])
self.connect_input_generator(syn3, 's_exglu', self.exglu[-1])
elif syntype in ['my_spillover_stp']:
syn1 = self.cell.insert_synapse('AMPA_stp', self.cell.dendlist[loc], pos,
add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn1, 'AMPA_stp', deterministic = deterministic, numsyn = i, tstart = start)
if self.cell.spines != []:
spines = [s for s in self.cell.spines if s.parent == self.cell.dendlist[loc]]
spines[i].syn_on = 0
syn2 = self.cell.insert_synapse('NMDA_stp', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 1)
self.connect_input_generator(syn2, 'NMDA_stp', syn1.stim[-1])
syn3 = self.cell.insert_synapse('NMDAe', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 0)
syn1.clustered_flag = syn2.clustered_flag = syn3.clustered_flag = True
self.connect_input_generator(self.exglu[-1], 't_exglu', syn1.stim[-1])
self.connect_input_generator(syn3, 's_exglu', self.exglu[-1])
elif syntype == 'no_spillover':
syn1 = self.cell.insert_synapse('AMPA', self.cell.dendlist[loc], pos,
add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn1, 'AMPA', deterministic = deterministic, numsyn = i, tstart = start)
if self.cell.spines != []:
spines = [s for s in self.cell.spines if s.parent == self.cell.dendlist[loc]]
spines[i].syn_on = 0
syn2 = self.cell.insert_synapse('NMDA', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 1)
self.connect_input_generator(syn2, 'NMDA', syn1.stim[-1])
syn1.clustered_flag = syn2.clustered_flag = True
elif syntype == 'no_spillover_stp':
syn1 = self.cell.insert_synapse('AMPA_stp', self.cell.dendlist[loc], pos,
add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn1, 'AMPA_stp', deterministic = deterministic, numsyn = i, tstart = start)
if self.cell.spines != []:
spines = [s for s in self.cell.spines if s.parent == self.cell.dendlist[loc]]
spines[i].syn_on = 0
syn2 = self.cell.insert_synapse('NMDA_stp', self.cell.dendlist[loc], pos,
add_spine = 0, on_spine = 1)
self.connect_input_generator(syn2, 'NMDA_stp', syn1.stim[-1])
syn1.clustered_flag = syn2.clustered_flag = True
if syntype == 'my_spillover':
self.set_exglu_weights()
elif syntype == 'pf':
for loc in syn_loc:
print(loc, p.input_dends.index(loc))
syn_step = 1.0/num_syns
cluster_start_pos = p.cluster_start_poss[p.input_dends.index(loc)]
cluster_end_pos = p.cluster_end_poss[p.input_dends.index(loc)]
for i in range(0, num_syns):
pos = cluster_start_pos + (cluster_end_pos - cluster_start_pos)*i*syn_step
start = rnd.uniform(p.plateau_burst_start, p.plateau_burst_end)
syn1 = self.cell.insert_synapse('AMPA_pf', self.cell.dendlist[loc],
pos, add_spine = add_spine, on_spine = on_spine)
self.add_input_generator(syn1, 'AMPA_pf', deterministic = deterministic,
numsyn = i, tstart = start )
syn2 = self.cell.insert_synapse('NMDA_pf', self.cell.dendlist[loc],
pos, add_spine = add_spine, on_spine = on_spine)
self.connect_input_generator(syn2, 'NMDA_pf', syn1.stim[-1])
# elif (type(syn_loc) == dict):
# for ind, loc in enumerate(syn_loc['loc']):
# for i in range(0, p.plateau_cluster_size):
# syn = self.cell.insert_synapse('glutamate_ica_nmda', self.cell.dendlist[loc],
# syn_loc['pos'][ind], add_spine = add_spine, on_spine = on_spine)
# self.add_input_generator(syn, 'glutamate_ica_nmda', 1.0 , tstart = syn_loc['start'][ind],
# tend = syn_loc['end'][ind], deterministic = deterministic, numsyn = i)
def add_input_generator(self, syn, syntype, freq_multiplier = 1,
tstart = p.plateau_burst_start, tend = p.plateau_burst_end, deterministic = 0, numsyn = 1):
if deterministic == 1:
noise = 0
start = tstart
number = p.num_spikes
interval = p.net_con_interval
weight = p.gAMPAmax_plateau
if syntype in ['input_syn']:
weight = p.gAMPAmax
elif syntype in ['NMDA', 'AMPA', 'NMDAe', 'AMPA_stp', 'NMDA_stp']:
weight = p.weight
elif syntype in ['inhexpsyn_plateau']:
weight = p.gGABAmax_plateau
number = 1
elif deterministic == 0:
noise = 1
if syntype in ['ramp']:
start = p.ramp_burst_start
end = p.ramp_burst_end
number = (end-start) * p.ramp_syn_rate
interval = p.ramp_syn_interval
weight = p.g_ramp_max
elif syntype in ['inhexpsyn', 'inhexp2syn']:
start = 0;
end = p.simtime
number = (end-start) * p.irate * freq_multiplier
interval = p.i_interval/freq_multiplier
weight = p.g_inhexpsyn_max
elif syntype in ['inhexpsyn_plateau']:
start = tstart
end = p.inhibitory_burst_end
number = p.num_spikes
interval = 0
weight = p.gGABAmax_plateau
elif syntype in ['expsyn_plateau', 'plateau_cluster', 'nmda_plateau', 'tmGlut',
'glutamate_ica_nmda',
'glutamate_plateau',
'adaptive_glutamate_hom',
'glutamate_xor_test',
'AMPA', 'NMDA', 'NMDAe',
'AMPA_stp', 'NMDA_stp',
'adaptive_AMPA', 'adaptive_NMDA',
'adaptive_sAMPA', 'adaptive_sNMDA',
'adaptive_hom_AMPA', 'adaptive_hom_NMDA',
'AMPA_test', 'NMDA_test','adaptive_NMDAe',
'adaptive_shom_AMPA', 'adaptive_shom_NMDA', 'adaptive_my_shom_NMDA',
'adaptive_shom_AMPA_stp', 'adaptive_shom_NMDA_stp',
'adaptive_glutamate_shom',
'adaptive_cshom_AMPA', 'adaptive_cshom_NMDA',
'adaptive_glutamate_cshom', 'adaptive_sglutamate', 'NMDAe',
'adaptive_zahra_NMDA','adaptive_pf_AMPA', 'adaptive_pf_NMDA']:
start = tstart; end = tend
number = p.num_spikes#(end-start) * p.plateau_syn_rate
interval = p.deterministic_interval#p.plateau_syn_interval
weight = 1.0
if syntype in ['expsyn_plateau', 'tmGlut',
'glutamate_plateau']:
weight = p.gAMPAmax_plateau
elif syntype in ['plateau_cluster', 'nmda_plateau', 'generalized_rule',
'generalized_rule_dist']:
weight = p.gNMDAmax_plateau
elif syntype in ['nmda']:
weight = p.gNMDAmax
elif syntype in ['NMDA', 'AMPA', 'NMDA_stp', 'AMPA_stp']:
weight = p.weight
elif syntype in ['AMPA_pf', 'NMDA_pf', 'adaptive_pf_AMPA', 'adaptive_pf_NMDA']:
start = p.pf_input_start
end = p.pf_input_end
number = p.pf_num_spikes
interval = p.pf_input_interval
weight = p.weight
elif syntype in ['input_syn']:
start = p.distributed_input_start
end = p.distributed_input_end
number = (end-start) * p.distributed_input_rate
interval = p.distributed_input_interval
weight = p.gAMPAmax
elif syntype in ['adaptive_glutamate']:
start = tstart; end = tend
number = (end-start) * p.low_rate
interval = p.low_interval
weight = 1.0
elif syntype in ['expsyn', 'exp2syn', 'glutamate']:
start = 0;
end = p.simtime
number = (end-start) * p.erate * freq_multiplier
interval = p.e_interval/freq_multiplier
weight = p.g_expsyn_max
gen = h.NetStim(0.5, sec = self.presyn)
gen.seed(int(time.time() + rnd.randint(1,10**7)))
gen.start = start
gen.noise = noise
gen.number = number
gen.interval = interval
nc = h.NetCon(gen, syn.obj)
nc.delay = 0
nc.weight[0] = weight
if syntype in ['ramp']:
self.ramp_estim.append(gen)
self.ramp_enc.append(nc)
elif syntype in ['inhexpsyn', 'inhexp2syn']:
syn.stim.append(gen)
syn.nc.append(nc)
elif syntype in ['inhexpsyn_plateau']:
syn.stim.append(gen)
syn.nc.append(nc)
elif syntype in ['expsyn', 'exp2syn', 'plateau_cluster', 'input_syn',
'expsyn_plateau', 'nmda_plateau', 'tmGlut', 'glutamate',
'adaptive_glutamate', 'glutamate_ica_nmda', 'glutamate_plateau',
'pf',
'AMPA', 'NMDA', 'NMDAe', 'adaptive_AMPA', 'NMDA_stp', 'AMPA_stp',
'adaptive_NMDA', 'adaptive_hom_AMPA', 'adaptive_hom_NMDA',
'AMPA_test', 'NMDA_test','adaptive_shom_AMPA', 'adaptive_shom_NMDA',
'adaptive_shom_AMPA_stp', 'adaptive_shom_NMDA_stp', 'adaptive_my_shom_NMDA',
'adaptive_NMDAe', 'adaptive_glutamate_shom','adaptive_cshom_AMPA',
'adaptive_cshom_NMDA','adaptive_glutamate_cshom', 'adaptive_sAMPA', 'adaptive_sNMDA',
'adaptive_sglutamate', 'adaptive_zahra_NMDA', 'AMPA_pf', 'NMDA_pf']:
syn.stim.append(gen)
syn.nc.append(nc)
def set_up_recording(self, dend_record_list = [], record_step = p.record_step):
self.dend_record_list = dend_record_list
self.vdlist = []
self.t = h.Vector()
self.t.record(h._ref_t, record_step)
self.tv = h.Vector()
self.tv.record(h._ref_t, p.record_step_v)
self.tthresh = h.Vector()
self.tthresh.record(h._ref_t, p.record_step_thresh)
self.cali = []
self.cali_dend = []
self.cai_nmda = []
self.cai = []
self.cati = []
self.cai_nmda_spine = []
self.cai_spine = []
self.cali_spine = []
self.cati_spine = []
self.vspine = []
self.kernel = []
self.kernel_LTD = []
self.cao = []
self.vs = []
self.vs = h.Vector()
self.vs.record(self.cell.somalist[0](0.5)._ref_v, p.record_step_v)
self.Cdur = []
if self.exptype == 'record_ca':
self.gk = []
self.m = []
self.ica_nmda = []
self.cai_soma = h.Vector()
self.cai_soma.record(self.cell.somalist[0](0.5)._ref_cai, record_step)
for intfire in self.exglu:
self.m.append(h.Vector())
self.m[-1].record(intfire._ref_m, record_step)
for d in dend_record_list:
self.vdlist.append(h.Vector())
self.vdlist[-1].record(self.cell.dendlist[d](p.pos)._ref_v, p.record_step_v)
self.gk.append(h.Vector())
self.gk[-1].record(self.cell.dendlist[d](p.pos)._ref_gk_kaf, record_step)
self.cai.append(h.Vector())
self.cali.append(h.Vector())
# self.cati.append(h.Vector())
self.cai_nmda.append(h.Vector())
self.cali_dend.append(h.Vector())
pos = p.cluster_start_poss[p.input_dends.index(d)]
self.cai[-1].record(self.cell.dendlist[d](pos)._ref_cai, record_step)
self.cali[-1].record(self.cell.dendlist[d](pos)._ref_cali, record_step)
self.cai_nmda[-1].record(self.cell.dendlist[d](pos)._ref_ca_nmdai, record_step)
self.cali_dend[-1].record(self.cell.dendlist[d](pos)._ref_cali, record_step)
if self.cell.spines != []:
self.record_spinelist = [s for s in self.cell.spines if s.parent == self.cell.dendlist[d] and s.syn_on == 1]
if p.include_empty_spines:
self.record_spinelist.extend([s for s in self.cell.spines if s.parent == self.cell.dendlist[d] and s.syn_on == 0])
for spine in self.record_spinelist:
# spine = self.record_spinelist[0]
self.vspine.append(h.Vector())
self.vspine[-1].record(spine.head(0.5)._ref_v, p.record_step_v)
self.cali_spine.append(h.Vector())
self.cali_spine[-1].record(spine.head(0.5)._ref_cali, record_step)
self.cati_spine.append(h.Vector())
self.cati_spine[-1].record(spine.head(0.5)._ref_cati, record_step)
self.cao.append(h.Vector())
self.cao[-1].record(spine.head(0.5)._ref_cao, record_step)
self.cai_spine.append(h.Vector())
self.cai_spine[-1].record(spine.head(0.5)._ref_cai, record_step)
self.cai_nmda_spine.append(h.Vector())
self.cai_nmda_spine[-1].record(spine.head(0.5)._ref_ca_nmdai, record_step)
# self.ical = []
# self.ical.append(h.Vector())
# self.ical[-1].record(spine.head(0.5)._ref_ical, record_step)
self.ica_nmda = []
self.ica_nmda.append(h.Vector())
self.ica_nmda[-1].record(spine.head(0.5)._ref_ica_nmda, record_step)
self.ica = []
self.ica.append(h.Vector())
self.ica[-1].record(spine.head(0.5)._ref_ica, record_step)
if self.exptype == 'record_i':
self.ina = []
self.ik = []
self.iampa = []
self.iNMDA = []
self.ica_nmda = []
for d in dend_record_list:
self.vdlist.append(h.Vector())
self.vdlist[-1].record(self.cell.dendlist[d](p.pos)._ref_v, p.record_step_v)
self.ik.append(h.Vector())
self.ik[-1].record(self.cell.dendlist[d](p.pos)._ref_ik, record_step)
self.ina.append(h.Vector())
self.ina[-1].record(self.cell.dendlist[d](p.pos)._ref_ina, record_step)
ampa_syns = [s for s in self.cell.esyn if s.sec == self.cell.dendlist[d] and s.type == 'AMPA']
nmda_syns = [s for s in self.cell.esyn if s.sec == self.cell.dendlist[d] and s.type == 'NMDA']
for s in ampa_syns:
self.iampa.append(h.Vector())
self.iampa[-1].record(s.obj._ref_iAMPA, record_step)
for s in nmda_syns:
self.iNMDA.append(h.Vector())
self.iNMDA[-1].record(s.obj._ref_iNMDA, record_step)
self.ica_nmda.append(h.Vector())
self.ica_nmda[-1].record(s.obj._ref_ica_nmda, record_step)
self.gsyn = []
self.inmda = []
self.A = []
self.B = []
self.w_ampa = []
self.w_nmda = []
self.lthresh_LTP = []
self.hthresh_LTP = []
self.lthresh_LTD = []
self.cali_agh = []
self.cati_agh = []
self.cai_agh = []
self.cai_nmda_agh = []
self.v_agh = []
self.kernel_agh = []
self.kernel_LTD_agh = []
self.delta_LTP = []
def plot_results(self):
sns.set(font_scale = 1.5)
sns.set_style("ticks")
if self.exptype == 'record_ca':
fig_vs = plt.figure()
ax_vs = fig_vs.add_subplot(111)
ax_vs.set_ylabel('Vs')
ax_vs.set_xlabel('t')
ax_vs.plot(self.tv, self.vs)
# fig_cai_soma = plt.figure()
# ax_cai_soma = fig_cai_soma.add_subplot(111)
# ax_cai_soma.set_ylabel('soma cai')
# ax_cai_soma.set_xlabel('t')
# ax_cai_soma.plot(self.tout, self.cai_soma)
fig_vd = plt.figure();
ax_vd = fig_vd.add_subplot(111);
ax_vd.set_ylabel('Vd')
ax_vd.set_xlabel('t')
for i in range(0,len(self.vdlist)):
ax_vd.plot(self.tv, self.vdlist[i])
fig_m = plt.figure();
ax_m = fig_m.add_subplot(111);
ax_m.set_ylabel('Intfire m')
ax_m.set_xlabel('t')
for i in range(0,len(self.m)):
ax_m.plot(self.tout, self.m[i])
legend_list = []
for i in self.dend_record_list:
legend_list.append('dend[%d](%.2f) = %.2f um' % (i,p.pos, h.distance(p.pos, sec = self.cell.dendlist[i]) ))
# fig_gk = plt.figure()
# ax_gk = fig_gk.add_subplot(111)
# ax_gk.set_ylabel('gk')
# ax_gk.set_xlabel('t')
# for i in range(0,len(self.gk)):
# ax_gk.plot(self.tout, self.gk[i])
# fig_cai = plt.figure();
# ax_cai = fig_cai.add_subplot(111);
# ax_cai.set_ylabel('[Ca]_i')
# ax_cai.set_xlabel('t')
# for i in range(0,len(self.cai)):
# ax_cai.plot(self.tout, self.cai[i])
#
# fig_cao = plt.figure();
# ax_cao = fig_cao.add_subplot(111);
# ax_cao.set_ylabel('[Ca]_o')
# ax_cao.set_xlabel('t')
# for i in range(0,len(self.cao)):
# ax_cao.plot(self.tout, self.cao[i])
#
#
# fig_cali = plt.figure();
# ax_cali = fig_cali.add_subplot(111);
# ax_cali.set_ylabel('[Cal]_i')
# ax_cali.set_xlabel('t')
# for i in range(0,len(self.cali)):
# ax_cali.plot(self.tout, self.cali[i])
#
# fig_cai_nmda = plt.figure();
# ax_cai_nmda = fig_cai_nmda.add_subplot(111);
# ax_cai_nmda.set_ylabel('[Ca]_NMDA')
# ax_cai_nmda.set_xlabel('t')
# for i in range(0,len(self.cai_nmda)):
# ax_cai_nmda.plot(self.tout, self.cai_nmda[i])
if self.cell.spines != []:
fig_vspine = plt.figure()
ax_vspine = fig_vspine.add_subplot(111)
ax_vspine.set_ylabel('Vspine')
ax_vspine.set_xlabel('t')
for v in self.vspine:
ax_vspine.plot(self.tv, v)
fig_ica = plt.figure();
ax_ica = fig_ica.add_subplot(111);
ax_ica.set_ylabel('spine Ica')
ax_ica.set_xlabel('t')
for i in range(0,len(self.ica)):
ax_ica.plot(self.tout, self.ica[i])
# fig_cai_nmda_spine = plt.figure();
# ax_cai_nmda_spine = fig_cai_nmda_spine.add_subplot(111);
# ax_cai_nmda_spine.set_ylabel('spine [Ca]_NMDA')
# ax_cai_nmda_spine.set_xlabel('t')
# for i in range(0,len(self.cai_nmda_spine)):
# ax_cai_nmda_spine.plot(self.tout, self.cai_nmda_spine[i])
#
# fig_cai_spine = plt.figure();
# ax_cai_spine = fig_cai_spine.add_subplot(111);
# ax_cai_spine.set_ylabel('spine [Ca]')
# ax_cai_spine.set_xlabel('t')
# for i in range(0,len(self.cai_spine)):
# ax_cai_spine.plot(self.tout, self.cai_spine[i])
#
# fig_cati_spine = plt.figure();
# ax_cati_spine = fig_cati_spine.add_subplot(111);
# ax_cati_spine.set_ylabel('spine [Cat]')
# ax_cati_spine.set_xlabel('t')
# for i in range(0,len(self.cai_spine)):
# ax_cati_spine.plot(self.tout, self.cati_spine[i])
#
# fig_cali_spine = plt.figure();
# ax_cali_spine = fig_cali_spine.add_subplot(111);
# ax_cali_spine.set_ylabel('spine [Cal]')
# ax_cali_spine.set_xlabel('t')
# for i in range(0,len(self.cali_spine)):
# ax_cali_spine.plot(self.tout, self.cali_spine[i])
#
# fig_cali_dend = plt.figure()
# ax_cali_dend = fig_cali_dend.add_subplot(111)
# ax_cali_dend.set_ylabel('[Cal]_i dend')
# ax_cali_dend.set_xlabel('t')
# for c in self.cali_dend:
# ax_cali_dend.plot(self.tout, c)
legend_list = []
for i in self.dend_record_list:
legend_list.append('dend[%d](%.2f) = %.2f um' % (i, p.pos, h.distance(p.pos, sec = self.cell.dendlist[i]) ))
# ax_vd.legend(legend_list); ax_cai.legend(legend_list);
# ax_cali.legend(legend_list); ax_cai_nmda.legend(legend_list);
# if self.cell.spines != []:
# ax_cali_dend.legend(legend_list)
plt.show()
# return fig_vs, fig_vd, fig_cali, fig_cai_nmda
if self.exptype in ['record_i']:
# fig_vs = plt.figure()
# ax_vs = fig_vs.add_subplot(111)
# ax_vs.set_ylabel('Vs')
# ax_vs.set_xlabel('t')
# ax_vs.plot(self.tv, self.vs)
#
# fig_vd = plt.figure();
# ax_vd = fig_vd.add_subplot(111);
# ax_vd.set_ylabel('Vd')
# ax_vd.set_xlabel('t')
# for i in range(0,len(self.vdlist)):
# ax_vd.plot(self.tv, self.vdlist[i])
#
# legend_list = []
# for i in self.dend_record_list:
# legend_list.append('dend[%d](%.2f) = %.2f um' % (i,p.pos, h.distance(p.pos, sec = self.cell.dendlist[i]) ))
fig_ik = plt.figure()
ax_ik = fig_ik.add_subplot(111)
ax_ik.set_ylabel('ik')
ax_ik.set_xlabel('t')
for i in range(0,len(self.ik)):
ax_ik.plot(self.tout, self.ik[i])
fig_ica_nmda = plt.figure();
ax_ica_nmda = fig_ica_nmda.add_subplot(111);
ax_ica_nmda.set_ylabel('ica_NMDA')
ax_ica_nmda.set_xlabel('t')
ax_ica_nmda.plot(self.tout, sum(self.ica_nmda))
fig_ina = plt.figure()
ax_ina = fig_ina.add_subplot(111)
ax_ina.set_ylabel('ina')
ax_ina.set_xlabel('t')
for i in range(0,len(self.ina)):
ax_ina.plot(self.tout, self.ina[i])
# fig_iampa = plt.figure()
# ax_iampa = fig_iampa.add_subplot(111)
# ax_iampa.set_ylabel('iampa')
# ax_iampa.set_xlabel('t')
# ax_iampa.plot(self.tout, sum(self.iampa))
fig_inmda = plt.figure()
ax_inmda = fig_inmda.add_subplot(111)
ax_inmda.set_ylabel('inmda')
ax_inmda.set_xlabel('t')
ax_inmda.plot(self.tout, sum(self.iNMDA))
fig_i = plt.figure()
ax_i = fig_i.add_subplot(111)
ax_i.set_ylabel('i')
ax_i.set_xlabel('t')
for i in range(0,len(self.ik)):
ax_i.plot(self.tout, self.ik[i]+self.ina[i]+sum(self.ica_nmda[i])+sum(self.iampa)+sum(self.iNMDA))
plt.show()
return fig_i, fig_ik, fig_inmda, fig_ica_nmda, fig_ina
def set_up_experiment(self):
if self.exptype in ['xor', 'xor_hom', 'xor_test_set', 'xor_gen', 'xor_spillover',
'xor_hom_spillover', 'xor_spillover_test','xor_shom_spillover',
'xor_cshom_spillover', 'xor_sspillover', 'xor_zahra_spillover',
'xor_shom_my_spillover', 'xor_shom_my_spillover_stp']:
self.insert_synapses('noise_SPN')
self.create_dopamine()
ts = self.create_training_set(p.training_set_size)
print("Training set"); print(ts)
self.training_set = ts; self.training_set_copy = self.training_set
self.rewards_delivered = []
self.xor_input_times, self. pf_input_times, self.reward_times = self.convert_ts_to_times(ts)
self.create_xor_inputs(p.xor_input_size)
self.connect_xor_inputs()
self.spike_recorder(self.cell.somalist[0], 0.5, -10)
def simulate(self, simtime = p.simtime, parallel = False):
start = time.time()
gmtime = time.gmtime(start)
print("Starting simulation... %d:%d:%d" %(gmtime.tm_hour + 1, gmtime.tm_min, gmtime.tm_sec))
if not parallel:
h.load_file("stdrun.hoc")
h.init()
h.tstop = simtime
fih3 = h.FInitializeHandler((self.seti_print_status))
h.run()
end = time.time()
print("It took %.2f hours or %.4f seconds to simulate." % ((end-start)/3600 , (end-start)))
if p.simtime > 1000:
self.tout = np.multiply(np.asarray(self.t.to_python()), 0.001)
else:
self.tout = self.t.to_python()
def get_recording_points(self, dend, step):
points = []
if dend.L < step:
points.append(1.0)
else:
q = int(dend.L/step)
for q in range(1,q+1):
points.append(q*step/dend.L)
points.append(1.0)
return points
def seti_stimulus_indicators(self, xor_input_times):
times = []
for x in xor_input_times:
times.extend(x)
times.sort()
times = (np.unique(times)).tolist()
for t in times:
h.cvode.event(t, (self.seti_stimulus_flag, 1))
def seti_stimulus_flag(self, flag):
h.stimulus_flag = flag
if flag:
h.cvode.event(h.t + p.training_input_length + p.time_to_reward + p.reward_length, (self.seti_stimulus_flag, 0))
else:
if self.exptype in ['xor_shom_my_spillover', 'xor_shom_my_spillover_stp']:
self.set_exglu_weights()
self.reset_exglu()
def seti_print_status(self):
update_points = np.arange(0, p.simtime, p.simtime/100. )
for t in update_points:
h.cvode.event(t, self.print_status)
def reset_exglu(self):
for s in self.exglu:
s.m = 0
def set_exglu_weights(self):
if self.exptype == 'xor_shom_my_spillover':
synlist = self.get_synapse_list('adaptive_my_shom_NMDA', clustered_flag = True)
for s, exglu in zip(synlist, self.exnc):
print(exglu.weight[0], s.obj.weight)
exglu.weight[0] = s.obj.weight*p.exglu_norm_factor
print(exglu.weight[0])
elif self.exptype == 'xor_shom_my_spillover_stp':
synlist = self.get_synapse_list('adaptive_shom_NMDA_stp', clustered_flag = True)
for s, exglu in zip(synlist, self.exnc):
exglu.weight[0] = s.obj.weight*p.exglu_norm_factor
elif self.exptype == 'record_ca':
for exglu in self.exnc:
exglu.weight[0] = p.weight*p.exglu_norm_factor
else:
print("From method set_exglu_weights in %s" % type(self))
print("Exptype '%s' not supported" % self.exptype)
sys.exit(-1)
def print_status(self):
pass
# print("At time t %f, total simtime %f." % (h.t, p.simtime))
def random_dend_list(self, input_dends, p):
dend_list = []
for d in input_dends:
if rnd.random() < p:
dend_list.append(d)
return dend_list
def get_synapse_list(self, syntype, clustered_flag = False):
synlist = []
for syn in self.cell.esyn:
if syn.type == syntype and syn.clustered_flag == clustered_flag:
synlist.append(syn)
return synlist
def delete_everything(self):
self.dend_record_list = None
self.vdlist = None
self.t = None
self.tout = None
self.short_tout = None
self.glu = None
self.cali = None
self.cali_dend = None
self.cai_nmda = None
self.cai = None
self.vspine = None
self.vs = None
self.record_spinelist = None
self.vspine = None
self.ical = None
self.gsyn = None
self.inmda = None
self.A = None
self.B = None
self.w_ampa = None
self.w_nmda = None
self.lthresh_LTP = None
self.hthresh_LTP = None
self.lthresh_LTD = None
self.soma_recorder_nc = None
self.soma_recorder_tvec = None
self.recorder_nc = None
self.recorder_tvec = None
self.ramp_enc = None
self.ramp_estim = None
self.distributed_input = None
self.distributed_input_vectors = None
self.distributed_input_nc = None
self.distributed_input_list = None
self.xor_input = None
self.xor_input_times = None
self.xor_input_nc = None
self.xor_input_vectors = None
self.pf_input = None
self.pf_input_vectors = None
self.reward_times = None
self.rewards_delivered = None
self.cai_nmda_in_syns = None
self.dopamine_vec = None
self.enc = []
self.estim = []
self.cell.esyn = []
self.inc = []
self.istim = []
self.cell.isyn = []
self.g_nmda_pf = None
self.cai_nmda_in_syns = None
self.cali_in_syns = None
self.presyn = h.Section()
self.synlist = None
self.training_set = None
self.training_set_copy = None
self.training_mode = None