Reward modulated STDP (Legenstein et al. 2008)

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Accession:116837
"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
Reference:
1 . Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4:e1000180 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python; PCSIM;
Model Concept(s): Pattern Recognition; Spatio-temporal Activity Patterns; Reinforcement Learning; STDP; Biofeedback; Reward-modulated STDP;
Implementer(s):
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 = 0.01
        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
        
        p.MuPos = 0.4 
        p.MuNeg = 1.0
        p.alpha_M = 0.11
        
        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.W0_stdp = p.Wmax / 2.0 * ( p.alpha_M ** (1.0/ (1 - p.MuPos)) ) 
    
        p.stdpApos = p.Mu * ( p.W0_stdp ** (1 - p.MuPos) )
        p.stdpAneg = - p.Mu * p.alpha_M 

        p.samplingTime = int(ep.Tsim / (200 * ep.DTsim))  # sampling time for the histogram in number of simulation steps
        
        
        print "Wmax = ", p.Wmax
    
        
    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 
        
        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)
        
        
        # Connect the learning neurons to the liqduid
        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, 
                                                                                          mupos = p.MuPos,
                                                                                          muneg = p.MuNeg,
                                                                                          DATraceDelay = p.DATraceDelay, 
                                                                                          DAStdpRate = p.DAStdpRate, 
                                                                                          useFroemkeDanSTDP = False, 
                                                                                          useMorrisonSTDP = True,
                                                                                          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 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"]