import sys
sys.path.append('../packages/reward_gen/build')
from pyreward_gen import *
from pypcsimplus import *
import pypcsimplus as pcsim
from numpy import *
import numpy
class Biofeed(pcsim.Model):
def defaultParameters(self):
p = self.params
ep = self.expParams
p.NumSyn = 100
p.numAdditionalTargetSynapses = 10
p.scaleAdditionalTargetSynapses = 0.5
p.ratioStrong = 0.5
p.numInhibSynapses = 0
# STDP Parameters
p.Mu = 0.00025
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'
# Kappa kernel
p.rewardDelay = 0.4
p.KappaAlpha = 1.01
p.KappaTaupos = 30e-3
p.KappaTauneg = 30e-3
p.KappaTaupos2 = 4e-3
p.KappaTauneg2 = 4e-3
p.KappaTauposSquare = 50e-3
p.KappaTaunegSquare = 50e-3
p.KappaGap = 1e-4
p.KappaTe = 1e-3
p.KernelType = 'DblExp'
p.KappaAnegSquare = -1.0
# synapse parameters
p.synTau = 5e-3
p.delaySyn = 1e-3
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
# some problem
p.Vthresh = - 59e-3
p.Vresting = - 70e-3
p.Trefract = 5e-3
p.Iinject = 0
p.Rbase = 1.1e-9
p.condWscale = 0.01
p.condWExcScale = 1.0
p.condWInhScale = 1.0
p.Wscale = 0.0174
p.WExcScale = 1.0
p.WInhScale = 1.0
p.initLearnWVar = 1.0 / 10
p.initLearnWBound = 2.0 / 10
p.initInhWMean = 1.0/4
p.initInhWVar = 1.0/16
p.initInhWBound = 1.0/8
p.noiseType = 'white'
p.OUScale = 1.0
return p
#
# Generate the model
#
def generate(self):
p = self.params
ep = self.expParams
dm = self.depModels
m = self.elements
net = self.net
p.synTauInh = p.synTau
p.Vreset = p.Vresting
p.Vinit = p.Vresting
p.noiseLvlDurations = [ ep.Tsim / 4.0, ep.Tsim / 4.0, ep.Tsim / 4.0, ep.Tsim ]
# setup the weights
tau_m = p.Cm * p.Rm
tau_s = p.synTau
p.condWeightExc = ((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.condWeightInh = ((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))))
# setup the weights of current synapses
tau_m = p.Cm * p.Rm
tau_s = p.synTau
p.weightExc = ((p.Vthresh - p.Vinit) * p.WExcScale * p.Wscale)/ ( 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.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.CondWmax = p.condWeightExc * 2
p.CondWmaxInh = p.condWeightInh * 2
p.Wmax = p.weightExc * 2
p.WmaxInh = p.weightInh * 2
p.numStrongTargetSynapses = int(p.NumSyn * p.ratioStrong)
p.numWeakTargetSynapses = p.NumSyn - p.numStrongTargetSynapses
p.stdpCondApos = p.Mu * p.CondWmax # is actually multiplication of the learning rate mu and Apos from stdp
p.stdpCondAneg = - p.alpha * p.stdpCondApos
p.stdpApos = p.Mu * p.Wmax # is actually multiplication of the learning rate mu and Apos from stdp
p.stdpAneg = - p.alpha * p.stdpApos
p.kappaScalePos = p.KappaTauposSquare / (p.KappaTaupos - p.KappaTaupos2)
p.KappaApos = p.KappaAlpha * p.kappaScalePos
p.KappaAposSquare = p.KappaAlpha
p.kappaScaleNeg = p.KappaTaunegSquare / (p.KappaTauneg - p.KappaTauneg2)
p.KappaAneg = - 1.0 * p.kappaScaleNeg
p.Inoise = p.Rbase
p.samplingTime = int(ep.Tsim / (200 * ep.DTsim)) # sampling time for the histogram in number of simulation steps
learnSynW = random.normal(1.0/2 * p.Wmax, p.initLearnWVar * p.Wmax, p.NumSyn)
learnSynW.clip( min = (1.0/2 - p.initLearnWBound )* p.Wmax , max = (1.0/2 + p.initLearnWBound )* p.Wmax)
targetSynW = hstack((ones(p.numStrongTargetSynapses) * p.Wmax, zeros(p.numWeakTargetSynapses)))
inhibSynW = random.normal(p.initInhWMean * p.WmaxInh, p.initInhWVar * p.WmaxInh,p.numInhibSynapses)
inhibSynW.clip( min = (p.initInhWMean + p.initInhWBound) * p.WmaxInh, max = (p.initInhWMean - p.initInhWBound) * p.WmaxInh)
additionalTargetSynW = p.scaleAdditionalTargetSynapses * ones(p.numAdditionalTargetSynapses) * p.Wmax
print "CondWmax = ", p.CondWmax
print "Wmax = ", p.Wmax
# ------------------------------------
learnCondSynW = list(p.CondWmax / p.Wmax * array(learnSynW))
targetCondSynW = hstack((ones(p.numStrongTargetSynapses) * p.CondWmax, zeros(p.numWeakTargetSynapses)))
inhibCondSynW = list(- p.CondWmaxInh / p.WmaxInh * array(inhibSynW))
additionalTargetCondSynW = p.scaleAdditionalTargetSynapses * ones(p.numAdditionalTargetSynapses) * p.CondWmax
#*************************************
# Setup the neurons
#*************************************
m.learning_nrn = net.add(DARecvLifNeuron(Cm = p.Cm,
Rm = p.Rm,
Vresting = p.Vresting,
Vthresh = p.Vthresh,
Vreset = p.Vreset,
Vinit = p.Vinit,
Trefract = p.Trefract,
Iinject = 0,
Inoise = p.Inoise), SimEngine.ID(0, 0 % (net.maxLocalEngineID() + 1)) )
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)
m.learning_cond_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 = 0,
Inoise = p.Inoise), SimEngine.ID(0, 1 % (net.maxLocalEngineID() + 1)))
m.target_nrn = net.add(LifNeuron(Cm = p.Cm,
Rm = p.Rm,
Vresting = p.Vresting,
Vthresh = p.Vthresh,
Vreset = p.Vreset,
Vinit = p.Vinit,
Trefract = p.Trefract), SimEngine.ID(0, 2 % (net.maxLocalEngineID() + 1)))
m.target_cond_nrn = net.add(CbLifNeuron(Cm = p.Cm,
Rm = p.Rm,
Vresting = p.Vresting,
Vthresh = p.Vthresh,
Vreset = p.Vreset,
Vinit = p.Vinit,
Trefract = p.Trefract), SimEngine.ID(0, 3 % (net.maxLocalEngineID() + 1)))
# Connect the learning and target neurons to the circuit
if p.DATraceShape == 'alpha':
DATraceResponse = AlphaFunctionSpikeResponse(p.DATraceTau)
else:
DATraceResponse = ExponentialDecaySpikeResponse(p.DATraceTau)
exc_permutation = numpy.random.permutation(dm.exc_nrn_popul.size())
read_exc_nrns = exc_permutation[:p.NumSyn]
addit_read_exc_nrns = exc_permutation[p.NumSyn:(p.NumSyn + p.numAdditionalTargetSynapses)]
read_inh_nrns = numpy.random.permutation(dm.inh_nrn_popul.size())[:p.numInhibSynapses]
# ******************************** Add learning synapses to learning_nrn
m.learning_plastic_syn = []
for i in xrange(p.NumSyn):
m.learning_plastic_syn.append(net.connect(dm.exc_nrn_popul[read_exc_nrns[i]], m.learning_nrn, DAModulatedStaticStdpSynapse2(
Winit= learnSynW[i],
tau = p.synTau,
delay = p.delaySyn,
Wex = p.Wmax,
activeDASTDP = True,
STDPgap = p.stdpGap,
Apos = p.stdpApos,
Aneg = p.stdpAneg,
taupos = p.stdpTaupos,
tauneg = p.stdpTauneg,
DAStdpRate = p.DAStdpRate,
useFroemkeDanSTDP = False,
daTraceResponse = DATraceResponse)))
m.learning_cond_plastic_syn = []
for i in xrange(p.NumSyn):
m.learning_cond_plastic_syn.append(net.connect(dm.exc_nrn_popul[read_exc_nrns[i]], m.learning_cond_nrn, DAModStdpStaticCondExpSynapse2(
Winit= learnCondSynW[i],
tau = p.synTau,
delay = p.delaySyn,
Wex = p.CondWmax,
activeDASTDP = True,
STDPgap = p.stdpGap,
Apos = p.stdpCondApos,
Aneg = p.stdpCondAneg,
taupos = p.stdpTaupos,
tauneg = p.stdpTauneg,
DAStdpRate = p.DAStdpRate,
useFroemkeDanSTDP = False,
daTraceResponse = DATraceResponse)))
m.target_syn = []
for i in xrange(p.NumSyn):
m.target_syn.append(net.connect(dm.exc_nrn_popul[read_exc_nrns[i]], m.target_nrn, StaticCurrExpSynapse(W = targetSynW[i],
delay = p.delaySyn,
tau = p.synTau)))
m.target_cond_syn = []
for i in xrange(p.NumSyn):
m.target_cond_syn.append(net.connect(dm.exc_nrn_popul[read_exc_nrns[i]], m.target_cond_nrn, StaticCondExpSynapse(W = targetCondSynW[i],
delay = p.delaySyn,
tau = p.synTau)))
m.addit_target_syn = []
for i in xrange(p.numAdditionalTargetSynapses):
m.addit_target_syn.append(net.connect(dm.exc_nrn_popul[addit_read_exc_nrns[i]], m.target_nrn, StaticCurrExpSynapse(W = additionalTargetSynW[i],
delay = p.delaySyn,
tau = p.synTau)))
m.addit_target_cond_syn = []
for i in xrange(p.numAdditionalTargetSynapses):
m.addit_target_cond_syn.append( net.connect( dm.exc_nrn_popul[addit_read_exc_nrns[i]],
m.target_cond_nrn, StaticCondExpSynapse( W = additionalTargetCondSynW[i],
delay = p.delaySyn,
tau = p.synTau)))
# Create the reward generator and connect it in the circuit
if p.KernelType == 'alpha':
rewardGenFactory = BioFeedRewardGenAlpha(Apos = KappaApos,
Aneg = p.KappaAneg,
taupos = p.KappaTaupos,
tauneg = p.KappaTauneg,
Gap = p.KappaGap,
Te = p.KappaTe)
elif p.KernelType == "DblExp":
rewardGenFactory = BioFeedRewardGenDblExp(Apos = p.KappaApos,
Aneg = p.KappaAneg,
taupos1 = p.KappaTaupos,
tauneg1 = p.KappaTauneg,
taupos2 = p.KappaTaupos2,
tauneg2 = p.KappaTauneg2,
Gap = p.KappaGap,
Te = p.KappaTe)
else:
rewardGenFactory = BioFeedRewardGen(Apos = p.KappaApos,
Aneg = p.KappaAneg,
taupos = p.KappaTaupos,
tauneg = p.KappaTauneg,
Gap = p.KappaGap,
Te = p.KappaTe)
m.reward_gen = net.add(rewardGenFactory, SimEngine.ID(0, 0))
net.connect(m.learning_nrn, 0, m.reward_gen, 1, Time.sec(ep.minDelay))
net.connect(m.target_nrn, 0, m.reward_gen, 0, Time.sec(ep.minDelay))
net.connect(m.reward_gen, 0, m.learning_nrn, 0, Time.sec(p.rewardDelay))
# ------------------------------
m.reward_gen_cond = net.add(rewardGenFactory, SimEngine.ID(0, 1 % (net.maxLocalEngineID() + 1)) )
net.connect(m.learning_cond_nrn, 0, m.reward_gen_cond, 1, Time.sec(ep.minDelay))
net.connect(m.target_cond_nrn, 0, m.reward_gen_cond, 0, Time.sec(ep.minDelay))
net.connect(m.reward_gen_cond, 0, m.learning_cond_nrn, 0, Time.sec(p.rewardDelay))
return self.elements
def setupRecordings(self):
m = self.elements
p = self.params
ep = self.expParams
#
# Recording all the weights
#
r = Recordings(self.net)
r.weights = SimObjectPopulation(self.net, m.learning_plastic_syn).record(AnalogRecorder(p.samplingTime), "W")
r.cond_weights = SimObjectPopulation(self.net, m.learning_cond_plastic_syn).record(AnalogRecorder(p.samplingTime), "W")
# Recorders for the two neurons
r.target_nrn_spikes = self.net.record(m.target_nrn, SpikeTimeRecorder())
r.target_cond_nrn_spikes = self.net.record(m.target_cond_nrn, SpikeTimeRecorder())
r.learning_nrn_spikes = self.net.record(m.learning_nrn, SpikeTimeRecorder())
r.learning_cond_nrn_spikes = self.net.record(m.learning_cond_nrn, SpikeTimeRecorder())
return r
def scriptList(self):
return ["BiofeedModel.py"]