snn_if_curr_exp.py

import spynnaker8 as sim

"""
A basic Spiking Neural Network(SNN) example constructed by using
SpikeSourcePoisson as stimuli in the input layer,
LIF neuron model (called as "cell type" in PyNN) in the post-synaptic population,
One-to-one connection in projection with
constant(fixed)-weighted StaticSynapse
"""

# === Parameters ===============================================================

n_neurons = 5   # number of neurons in each population for the Spiking Neural Network in this example
timestamp = 1.0 # simulate the network with 1.0 ms time steps
sim_time = 100  # total simulation time

# === Configure the simulator ==================================================

sim.setup( timestamp )

# === Build the network ========================================================

# Presynaptic population - Input layer - Stimuli
pop_input = sim.Population( n_neurons, sim.SpikeSourcePoisson( rate = 100, duration = sim_time ),
                            label = 'pop_input' )
# Postsynaptic population
pop_output = sim.Population( n_neurons, sim.IF_curr_exp, {}, # Use default values of LIF neuron
                             label = 'pop_output' )

# Synapse value as weight = 2.0 nA, delay = 2 ms
static_synapse = sim.StaticSynapse( weight = 2.0, delay = 2 )

"""
Notes:
    * When the connector is "AllToAllConnector" they all behave the same way, that's why I preferrred "OneToOneConnector" for this example.
    * Default "receptor_type" is excitatory.
    * "Space" is used to calculate distances. Default value for the variable "Space" is 3D.
        * It has parameters such as "scale_factor" (default=1.0) and "offset" (default=0.0) which is thought to be useful in topographic mapping.
        * Check out https://en.wikipedia.org/wiki/Topographic_map_%28neuroanatomy%29
"""

sim.Projection( pop_input,
                pop_output,
                connector = sim.OneToOneConnector(),
                synapse_type = static_synapse,
                receptor_type = 'excitatory',
                space = sim.Space( axes = 'xy' ),
                label = "LIF Projection"
              )

# == Instrument the network ====================================================

# Record all to observe.
pop_output.record( "all" )

# === Run the simulation =======================================================

sim.run( sim_time )

# === Plot the results =========================================================

from util.basic_visualizer import *
plot( [pop_output] )

# === Clean up and quit ========================================================

sim.end()