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]
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):
import os, sys
sys.path.append('../packages')
from pylab import *
from tables import *
from numpy import *
from math import *
from pypcsimplus import *
import pypcsimplus
import pypcsimplus.common
from pypcsimplus.clusterUtils import *
from frame import FrameAxes
import numpy
import time

from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Computer Modern Sans serif']})
rc('text', usetex=True)

random.seed(123525)

if len(sys.argv) > 1:
    h5filename = sys.argv[1]

else:
    h5filename = last_file(".*\.h5")
    
print " loading h5 filename : ", h5filename

h5file = openFile(h5filename, mode = "r", title = "Biofeedback DASTDP Experiment results")

p = constructParametersFromH5File(h5file)
r = constructRecordingsFromH5File(h5file)


other_circ_not_ou_weights = r.other_circ_not_ou_weights.copy()
other_circ_ou_weights = r.other_circ_ou_weights.copy()

other_circ_ou_weights *= 1.0/p.WHighOUScale
other_circ_not_ou_weights *= 1.0/p.WLowOUScale

other_weights = vstack((other_circ_not_ou_weights, other_circ_ou_weights))    

reinforced_circ_weights = vstack((r.reinforced_ou_weights, r.reinforced_other_weights))

reinforced_circ_weights *= 1.0/p.WLowOUScale

p.Wmax = 2 * p.Wexc

exc_ou_spikes = []
for i in r.exc_ou_nrn_idxs:
    exc_ou_spikes.append(r.spikes[i])
    
exc_other_spikes = []
for i in r.exc_other_nrn_idxs:
    exc_other_spikes.append(r.spikes[i])


reinforced_circ_avg_weights = average(reinforced_circ_weights, 0)
reinforced_circ_std_weights = std(reinforced_circ_weights, 0)

reinforced_ou_avg_weights = average(r.reinforced_ou_weights, 0)
reinforced_ou_std_weights = std(r.reinforced_ou_weights, 0)

reinforced_other_avg_weights = average(r.reinforced_other_weights, 0)
reinforced_other_std_weights = std(r.reinforced_other_weights, 0)

other_avg_weights = average(other_weights, 0)
other_std_weights = std(other_weights, 0)


other_circ_avg_not_ou_weights = average(other_circ_not_ou_weights, 0)
other_circ_std_not_ou_weights = std(other_circ_not_ou_weights, 0)

other_circ_avg_ou_weights = average(other_circ_ou_weights, 0)
other_circ_std_ou_weights = std(other_circ_ou_weights, 0)
    
analysis_intervals = [ (300,360), (600,660), (900,960), (1140,1200) ]

gray_colors = ['0.75', '0.6', '0.4', '0.0']
plot_colors = [ 'r', 'g', 'b', 'm' ]     

IntervalLength = analysis_intervals[0][1] - analysis_intervals[0][0]
    
w_hist_array = []
numBins = 30
for pl_i in range(len(analysis_intervals)):    
    ind_start = analysis_intervals[pl_i][0] / (p.Tsim / len(other_weights[0]))
    ind_end = analysis_intervals[pl_i][1] / (p.Tsim / len(other_weights[0]))                
    w_hist_array.append(histogram(mean(other_weights[:,ind_start:ind_end]/p.Wmax,1), bins=numBins, range = (0,1), normed = False)[0]/float(len(other_weights)))
                

avg_rates = []
numBins = 20        
for t in range(len(analysis_intervals)):
    avg_rates.append([])
    for i in range(len(r.spikes[:p.nExcNeurons])):
        avg_rates[t].append( calc_rate_2(clip_window(r.spikes[i], analysis_intervals[t][0], analysis_intervals[t][1]), 1, 1, IntervalLength) )

rate_hist_array = []
for t in range(len(analysis_intervals)):
    rate_hist_array.append(histogram(avg_rates[t], bins = numBins, range = (0,14), normed = False)[0]/float(len(avg_rates[t])))
    
#=============================================================================================    

f = figure(1, figsize = (8,6), facecolor = 'w')        
f.subplots_adjust(top= 0.95, left = 0.085, bottom = 0.11, right = 0.95, hspace = 0.36, wspace = 0.30)

clf()

plot_labels = ['300-360 sec', '600-660 sec', '900-960 sec', '1140-1200 sec' ]

ax = subplot(2,2,1, projection = 'frameaxes')
for pl_i in range(len(analysis_intervals)):
    plot(arange(0,1.0+0.9/len(w_hist_array[pl_i]),(1.0+1.0/len(w_hist_array[pl_i]))/len(w_hist_array[pl_i])), w_hist_array[pl_i], linewidth = 2, color = plot_colors[pl_i], label = plot_labels[pl_i] )

legend(prop = matplotlib.font_manager.FontProperties(size = 'xx-small'))
    
xlabel('synaptic weight $(w/w_{max})$')
ylabel('frac. synapses [\%]')    
yticks(arange(0,0.41,0.10), [ "%d" % (x) for x in arange(0, 0.41, 0.10)*100 ] )
xticks(arange(0,1.01,0.2), [ "%.1f" % (x) for x in arange(0,1.01,0.2) ] )
ylim(0,0.40)

text(-0.2,1.04, 'A',fontsize = 'x-large', transform = ax.transAxes)

plot_labels = ['300-360 sec', '600-660 sec', '900-960 sec', '1140-1200 sec' ]

ax = subplot(2,2,2, projection = 'frameaxes')
for pl_i in range(len(analysis_intervals)):
    plot(arange(0,16.0+15.9/len(rate_hist_array[pl_i]),(16.0+15.9/len(rate_hist_array[pl_i]))/len(rate_hist_array[pl_i])), rate_hist_array[pl_i], linewidth = 2, color = plot_colors[pl_i], label = plot_labels[pl_i] )
    
ylabel('frac. neurons [\%]')    
xlabel('firing rate [Hz]')
xticks(arange(0,16.1,4), [ "%d" % (x) for x in arange(0,16.1,4) ])    
yticks(arange(0, 0.201, 0.05), [ "%d" % (x) for x in arange(0, 0.201, 0.05)*100 ])
ylim(0, 0.201)

text(-0.2, 1.04, 'B', fontsize = 'x-large', transform = ax.transAxes)
    
legend(prop = matplotlib.font_manager.FontProperties(size = 'xx-small'))

IPcontroller = {'host' : 'cluster1', 'engine_port' : 32100, 'rc_port' : 32101, 'task_port' : 32102}
IPcluster = Cluster(ClusterConfig(configFile='./clusterconf.py', controller=IPcontroller))
IPcluster.start(waitafter = 3.0)
IPcluster.connect()
rc = IPcluster.getRemoteControllerClient()


print "len(rc) is ", len(rc)
while len(rc) < 30:
    print "len(rc) is ", len(rc)
    time.sleep(1)

rc.execute('import pypcsimplus')
rc.execute('from pypcsimplus import *')
rc.execute('import pypcsimplus.common')    


corrDT = 0.2e-3
leftCorrBound = -100e-3
rightCorrBound = 100e-3
numNrnPairs = 200
pairs = set([])
while len(pairs) < numNrnPairs:
    rnd_pair  = (random.randint(0,p.nExcNeurons-1),random.randint(0,p.nExcNeurons-1))
    if not rnd_pair[0] == rnd_pair[1]:
        pairs.add(rnd_pair)

i = 0
figure_letter = ['C', 'D']
for pl_i in (0,len(analysis_intervals)-1):
    print "processing cross_covariance for interval %d" % (pl_i)
    leftSimT = analysis_intervals[pl_i][0]
    rightSimT = analysis_intervals[pl_i][1]
    
    arg_list = []
    for n1, n2 in pairs:        
        arg_list.append([r.spikes[n1], r.spikes[n2], corrDT, (leftSimT, rightSimT), (leftCorrBound, rightCorrBound)])
                
    corr = rc.map(pypcsimplus.common.cross_covariance_spikes_1arg, arg_list)
    
    avg_corr = mean(vstack(tuple(corr)), 0)
    
    ax = subplot(2,2,i + 3, projection = 'frameaxes')     
    text(-0.2,1.04, figure_letter[i],fontsize = 'x-large', transform = ax.transAxes)
    vlines( arange(int(leftCorrBound/corrDT) * corrDT, int(rightCorrBound/corrDT) * corrDT + corrDT/100, corrDT), zeros(len(avg_corr)), avg_corr, linewidth = 1)
    xlim(leftCorrBound,rightCorrBound)
    xticks(arange(-100e-3,101e-3,50e-3), [  '%d' % (x) for x in arange(-100,101,50)])
    
    xlabel("$\\tau$ [ms]")
    ylabel("$C(\\tau)\quad (\\cdot 10^{-4})$")
    yticks(arange(-0.0005,0.00101,0.0005), [ "%d" % (x) for x in arange(-5,11,5)])
    
    ylim(-0.0005,0.00101)
    i += 1

    
IPcluster.stop()
    
savefig('correlations_weight_depend.eps')

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