from pypcsimplus import *
import pypcsimplus as pcsim
from numpy import *
import numpy
class ReadoutModel(pcsim.Model):
def defaultParameters(self):
p = self.params
# STDP Parameters
p.Mu = 0.0013
p.alpha = 1.05
p.stdpTaupos = 30e-3
p.stdpTauneg = 30e-3
p.stdpGap = 5e-4
# Dopamine Modulated STDP Parameters
p.DATraceDelay = 0.0
p.DATraceTau = 0.4
p.DAStdpRate = 3
p.DATraceShape = 'alpha'
p.KappaAnegSquare = -1.0
# synapse parameters
p.synTau = 5e-3
p.delaySyn = 1e-3
p.U = 0.5
p.D = 1.1
p.F = 0.02
p.Uinh = 0.25
p.Dinh = 0.7
p.Finh = 0.02
p.ErevExc = 0.0
p.ErevInh = -75e-3
# Neuron parameters
p.Cm = 3e-10
p.Rm = 1e8
p.Vthresh = - 59e-3
p.Vresting = - 70e-3
p.Vreset = -70e-3
p.Trefract = 5e-3
p.Iinject = 0.0e-10
p.Inoise = 0.0e-10
p.Wscale = 0.184
p.WExcScale = 1.0
p.WInhScale = 1.0
p.initLearnWVar = 1.0 / 10.0
p.initLearnWBound = 2.0 / 10.0
p.initInhWMean = 1.0/ 2.0
p.initInhWVar = 1.0/10
p.initInhWBound = 2.0/10
p.diminishingNoise = False
p.noiseSegments = 10
p.noiseType = 'OU'
p.OUScale = 0.2
return p
def derivedParameters(self):
p = self.params
ep = self.expParams
dm = self.depModels
m = self.elements
net = self.net
p.noiseLevels = [ p.Inoise * (10 - i) / 10 for i in arange(p.noiseSegments) ]
p.noiseDurations = [ ep.nTrainEpochs * ep.trialT / p.noiseSegments for i in range(p.noiseSegments) ]
p.synTauInh = 2 * p.synTau
p.Vinit = p.Vreset
# setup the weights
tau_m = p.Cm * p.Rm
tau_s = p.synTau
p.weightExc = ((p.Vthresh - p.Vinit) * p.WExcScale * p.Wscale)/ ((p.ErevExc - p.Vinit) * p.Rm * tau_s / (tau_m - tau_s) * ((tau_s / tau_m) ** (tau_s / (tau_m - tau_s)) - (tau_s / tau_m) ** (tau_m / (tau_m - tau_s))))
tau_s = p.synTauInh
p.weightInh = ((p.Vthresh - p.Vinit) * p.WInhScale * p.Wscale)/ (((p.Vinit+ p.Vthresh) / 2 - p.ErevInh) * p.Rm * tau_s / (tau_m - tau_s) * ((tau_s / tau_m) ** (tau_s / (tau_m - tau_s)) - (tau_s / tau_m) ** (tau_m / (tau_m - tau_s))))
p.Wmax = p.weightExc * 2.5
p.WmaxInh = p.weightInh * 2.5
p.stdpApos = p.Mu * p.Wmax
p.stdpAneg = - p.alpha * p.stdpApos
p.samplingTime = int(ep.Tsim / (200 * ep.DTsim)) # sampling time for the histogram in number of simulation steps
print "Wmax = ", p.Wmax
#
# Generate the model
#
def generate(self):
p = self.params
ep = self.expParams
dm = self.depModels
m = self.elements
net = self.net
#*************************************
# Setup the neurons
#*************************************
self.derivedParameters()
p.NumSyn = dm.input_channel_popul.size()
p.numInhibSynapses = 0 # dm.inh_nrn_popul.size() / 2
m.learnSynW = random.normal(1.0/2 * p.Wmax, p.initLearnWVar * p.Wmax, p.NumSyn)
m.learnSynW.clip( min = (1.0/2 - p.initLearnWBound )* p.Wmax , max = (1.0/2 + p.initLearnWBound )* p.Wmax)
m.inhibSynW = random.normal(p.initInhWMean * p.WmaxInh, p.initInhWVar * p.WmaxInh,p.numInhibSynapses)
m.inhibSynW.clip( min = (p.initInhWMean + p.initInhWBound) * p.WmaxInh, max = (p.initInhWMean - p.initInhWBound) * p.WmaxInh)
m.learning_nrn = net.add( DARecvCbLifNeuron(Cm = p.Cm,
Rm = p.Rm,
Vresting = p.Vresting,
Vthresh = p.Vthresh,
Vreset = p.Vreset,
Vinit = p.Vinit,
Trefract = p.Trefract,
Iinject = p.Iinject,
Inoise = p.Inoise), SimEngine.ID(0, 0) )
if p.noiseType == 'OU':
net.mount(OUNoiseSynapse(0.012e-6 * p.OUScale, 0.003e-6 * p.OUScale, 2.7e-3, 0.0), m.learning_nrn)
net.mount(OUNoiseSynapse(0.057e-6 * p.OUScale, 0.0066e-6 * p.OUScale, 10.5e-3,-75e-3), m.learning_nrn)
if p.DATraceShape == 'alpha':
DATraceResponse = AlphaFunctionSpikeResponse(p.DATraceTau)
else:
DATraceResponse = ExponentialDecaySpikeResponse(p.DATraceTau)
exc_permutation = numpy.random.permutation(dm.input_channel_popul.size())
read_exc_nrns = exc_permutation[:p.NumSyn]
# ******************************** Add learning synapses to learning_nrn
m.learning_plastic_syn = []
for i in xrange(p.NumSyn):
m.learning_plastic_syn.append(net.connect(dm.input_channel_popul[read_exc_nrns[i]], m.learning_nrn, DAModStdpDynamicCondExpSynapse(
Winit = m.learnSynW[i],
Erev = p.ErevExc,
U = p.U,
D = p.D,
F = p.F,
Wex = 1.0 * p.Wmax,
activeDASTDP = True,
STDPgap = p.stdpGap,
Apos = p.stdpApos,
Aneg = p.stdpAneg,
taupos = p.stdpTaupos,
tauneg = p.stdpTauneg,
DATraceDelay = p.DATraceDelay,
DAStdpRate = p.DAStdpRate,
useFroemkeDanSTDP = False,
daTraceResponse = DATraceResponse)))
if p.diminishingNoise == True:
m.noise_level_gen = net.add( AnalogLevelBasedInputNeuron(p.noiseLevels, p.noiseDurations), SimEngine.ID(0,0) )
net.connect(m.noise_level_gen, 0, m.learning_nrn, "Inoise", Time.sec(ep.minDelay))
return self.elements
def setTestPhase(self):
ep = self.expParams
net = self.net
m = self.elements
if not ep.testWithNoise:
if (net.object(m.learning_nrn)):
net.object(m.learning_nrn).Inoise = 0
for s in m.learning_plastic_syn:
if (net.object(s)):
net.object(s).activeDASTDP = False
def setTrainPhase(self):
net = self.net
m = self.elements
ep = self.expParams
if not ep.testWithNoise:
if net.object(m.learning_nrn):
net.object(m.learning_nrn).Inoise = self.params.Inoise
for s in m.learning_plastic_syn:
if (net.object(s)):
net.object(s).activeDASTDP = True
def switchOffRecordVmReadout(self):
self.net.object(self.recordings.learning_nrn_vm).setActive(False)
def switchOnRecordVmReadout(self):
self.net.object(self.recordings.learning_nrn_vm).setActive(True)
def increaseThreshold(self):
self.net.object(self.elements.learning_nrn).Vthresh = 0
def setNormalThreshold(self):
self.net.object(self.elements.learning_nrn).Vthresh = self.params.Vthresh
def printSamplingTime(self):
print "samplingTime is ", self.net.object(self.recordings.learning_nrn_vm).samplingTime
def setupRecordings(self):
m = self.elements
p = self.params
ep = self.expParams
#
# Recording all the weights
#
r = Recordings(self.net)
self.recordings = r
r.weights = SimObjectPopulation(self.net, m.learning_plastic_syn).record(AnalogRecorder(p.samplingTime), "W")
r.learning_spikes = self.net.record(m.learning_nrn, SpikeTimeRecorder())
if ep.recordReadoutVm:
r.learning_nrn_vm = self.net.record(m.learning_nrn, "Vm", AnalogRecorder())
else:
r.learning_nrn_vm = self.net.record(m.learning_nrn, "Vm", AnalogRecorder(p.samplingTime))
return r
def scriptList(self):
return ["ReadoutModel.py"]