#!/usr/bin/env python
import nest
import numpy as np
import pylab
import parameters as p
class SpikingNeuralNetwork():
def __init__(self):
nest.ResetKernel()
nest.SetKernelStatus({"local_num_threads" : 1, "resolution" : p.time_resolution})
self.spike_generators_l = nest.Create("poisson_generator", p.resolution[0]//2*p.resolution[1], params=p.poisson_params)
self.spike_generators_r = nest.Create("poisson_generator", p.resolution[0]//2*p.resolution[1], params=p.poisson_params)
self.neuron_l = nest.Create("iaf_psc_alpha", params=p.iaf_params)
self.neuron_r = nest.Create("iaf_psc_alpha", params=p.iaf_params)
self.spike_detector_l = nest.Create("spike_detector", params={"withtime": True})
self.spike_detector_r = nest.Create("spike_detector", params={"withtime": True})
self.multimeter_l = nest.Create("multimeter", params={"withtime":True, "record_from":["V_m"]})
self.multimeter_r = nest.Create("multimeter", params={"withtime":True, "record_from":["V_m"]})
weights_l = np.fliplr(p.weights_l.T).reshape(p.weights_l.size)
weights_r = np.fliplr(p.weights_r.T).reshape(p.weights_r.size)
for i in range(weights_l.size):
syn_dict = {"model": "static_synapse",
"weight": weights_l[i]}
nest.Connect([self.spike_generators_l[i]], self.neuron_l, syn_spec=syn_dict)
for i in range(weights_r.size):
syn_dict = {"model": "static_synapse",
"weight": weights_r[i]}
nest.Connect([self.spike_generators_r[i]], self.neuron_r, syn_spec=syn_dict)
nest.Connect(self.neuron_l, self.spike_detector_l)
nest.Connect(self.neuron_r, self.spike_detector_r)
nest.Connect(self.multimeter_l, self.neuron_l)
nest.Connect(self.multimeter_r, self.neuron_r)
def simulate(self, dvs_data):
time = nest.GetKernelStatus("time")
nest.SetStatus(self.spike_generators_l, {"origin": time})
nest.SetStatus(self.spike_generators_r, {"origin": time})
nest.SetStatus(self.spike_generators_l, {"stop": p.sim_time})
nest.SetStatus(self.spike_generators_r, {"stop": p.sim_time})
data_l = np.array(dvs_data[:dvs_data.shape[0]//2,:])
data_l = data_l.reshape(data_l.size)
data_r = np.array(dvs_data[dvs_data.shape[0]//2:,:])
data_r = data_r.reshape(data_r.size)
for i in range(data_l.size):
rate = data_l[i]/p.max_spikes
rate = np.clip(rate,0,1)*p.max_poisson_freq
nest.SetStatus([self.spike_generators_l[i]], {"rate": rate})
for i in range(data_r.size):
rate = data_r[i]/p.max_spikes
rate = np.clip(rate,0,1)*p.max_poisson_freq
nest.SetStatus([self.spike_generators_r[i]], {"rate": rate})
nest.Simulate(p.sim_time)
n_l = nest.GetStatus(self.spike_detector_l,keys="events")[0]["times"]
n_r = nest.GetStatus(self.spike_detector_r,keys="events")[0]["times"]
nest.SetStatus(self.spike_detector_l, {"n_events": 0})
nest.SetStatus(self.spike_detector_r, {"n_events": 0})
return n_l, n_r
