from pypcsimplus import *
import pypcsimplus as pcsim
from numpy import *
class PoissInput(pcsim.Model):
def defaultParameters(self):
p = self.params
# input parameters
p.inputRate = 13.5
p.nInputNeurons = 190
p.nExcNeurons = 150
p.Trefract = 5e-4
p.input_type = 'inputNeurons'
def setupRecordings(self):
p = self.params
r = Recordings(self.net)
if p.input_type != 'inputNeurons':
r.spikes = self.elements.input_nrn_popul.record(SpikeTimeRecorder())
else:
r.spikes = self.elements.inputs
return r
def generate(self):
m = self.elements
dm = self.depModels
p = self.params
ep = self.expParams
if p.input_type == 'inputNeurons':
m.input_nrn_popul = SimObjectPopulation(self.net, SpikingInputNeuron(), p.nInputNeurons)
m.inputs = sort(random.uniform(0, ep.Tsim - int(p.inputRate * ep.Tsim) * p.Trefract, m.input_nrn_popul.size() * int(p.inputRate * ep.Tsim)).reshape(m.input_nrn_popul.size(),
int(p.inputRate * ep.Tsim)), 1) + outer(ones(m.input_nrn_popul.size()), cumsum(ones(int(p.inputRate * ep.Tsim))*p.Trefract))
for i in range(m.input_nrn_popul.size()):
if m.input_nrn_popul.object(i):
m.input_nrn_popul.object(i).setSpikes(m.inputs[i])
else:
m.input_nrn_popul = SimObjectPopulation( self.net, LinearPoissonNeuron(1, 1, 5e-4, 0, p.inputRate), p.nInputNeurons )
exc_nrn_ids = []
inh_nrn_ids = []
for i in range(0,p.nExcNeurons):
exc_nrn_ids.append(m.input_nrn_popul[i])
m.exc_nrn_popul = SimObjectPopulation(self.net, exc_nrn_ids)
for i in range(p.nExcNeurons,m.input_nrn_popul.size()):
inh_nrn_ids.append(m.input_nrn_popul[i])
m.inh_nrn_popul = SimObjectPopulation(self.net, inh_nrn_ids)
return m
def scriptList(self):
return ["PoissInputModel.py"]
def getOutput(self, p):
return self.elements