def loadExperimentBind4 (prefix, x, y): startcount = 1 * 10000 # (learning time in s * FSV) listB = brainsim.loadBrain (prefix + str (x) + "-" + str(y) + "-BReport.txt") listG = brainsim.loadBrain (prefix + str (x) + "-" + str(y) + "-GReport.txt") listT = brainsim.loadBrain (prefix + str (x) + "-" + str(y) + "-TReport.txt") listS = brainsim.loadBrain (prefix + str (x) + "-" + str(y) + "-SReport.txt") cntB, spikesB = countSpikes (listB, startcount) cntG, spikesG = countSpikes (listG, startcount) cntT, spikesT = countSpikes (listT, startcount) cntS, spikesS = countSpikes (listS, startcount) return (spikesB,spikesG,spikesT,spikesS)
def genSpikePlotXY(prefix, num):
listX = brainsim.loadBrain(prefix + str(num) + '-XReport.txt')
listY = brainsim.loadBrain(prefix + str(num) + '-YReport.txt')
c = numpy.linspace(0.0, len(listX), len(listX), endpoint=False)
d = numpy.linspace(0.0, len(listY), len(listY), endpoint=False)
pyplot.subplot(211)
pyplot.title("Neuron X")
pyplot.plot(c,listX)
pyplot.subplot(212)
pyplot.title("Neuron Y")
pyplot.plot(d,listY)
pyplot.show()
def plotVoltage():
global parameters
sp=np.linspace(0.0,parameters['ENDSIM'],parameters['ENDSIM']*parameters['FSV'])
lB=brainsim.loadBrain('vb-exp-BReport.txt')
lG=brainsim.loadBrain('vb-exp-GReport.txt')
lT=brainsim.loadBrain('vb-exp-TReport.txt')
lS=brainsim.loadBrain('vb-exp-SReport.txt')
lI=brainsim.loadBrain('vb-exp-IReport.txt')
plt.subplot(511)
plt.plot(sp,lB,'b')
plt.subplot(512)
plt.plot(sp,lG,'b')
plt.subplot(513)
plt.plot(sp,lT,'b')
plt.subplot(514)
plt.plot(sp,lS,'b')
plt.subplot(515)
plt.plot(sp,lI,'b')
plt.show()
def plotUse():
global parameters
sp=np.linspace(0.0,parameters['ENDSIM'],
1 + parameters['ENDSIM'] * parameters['FSV'])
lB=brainsim.loadBrain('vb-exp-BUseReport.txt')
lG=brainsim.loadBrain('vb-exp-GUseReport.txt')
lT=brainsim.loadBrain('vb-exp-TUseReport.txt')
lS=brainsim.loadBrain('vb-exp-SUseReport.txt')
plt.subplot(221)
plt.title('Blue Use')
plt.plot(sp,lB,'g')
plt.subplot(222)
plt.title('Green Use')
plt.plot(sp,lG,'g')
plt.subplot(223)
plt.title('Triangle Use')
plt.plot(sp,lT,'g')
plt.subplot(224)
plt.title('Square Use')
plt.plot(sp,lS,'g')
plt.show()
def run ():
exp = 99
listX = brainsim.loadBrain ("vb-exp" + str (exp) + "-XReport.txt")
listY = brainsim.loadBrain ("vb-exp" + str (exp) + "-YReport.txt")
cntX, spikesX = countSpikes (listX)
cntY, spikesY = countSpikes (listY)
print cntX
print cntY
print spikesX
print spikesY
print "SIGNAL CORRELATION: ", scipy.correlate (spikesX, spikesY)
print "MAX: ", scipy.correlate (spikesX, spikesY).argmax()
print "Total spike correlation (Brian func.): ", totalCorrelation(spikesX,spikesY,cntX,cntY)
from matplotlib import pyplot
pyplot.subplot (211)
pyplot.plot (numpy.linspace (0.0, len (listX), len (listX)), listX)
pyplot.subplot (212)
pyplot.plot (numpy.linspace (0.0, len (listY), len (listY)), listY)
pyplot.show ()
def show3DPlot():
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FixedLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import brainsim
from countspikes import countSpikes
fig = plt.figure()
ax = fig.gca(projection='3d')
vals=np.array(range(0,10))
X = map(lambda x: float(x)/100, vals)
Y = map(lambda x: float(x)/10, vals[::-1]) # ISYN
X, Y = np.meshgrid(X, Y)
#R = np.sqrt(X**2 + Y**2)
#Z = np.sin(R)
listX=brainsim.loadBrain("vb-exp77-XReport.txt")
listY=brainsim.loadBrain("vb-exp77-YReport.txt")
cntX,spikesX=countSpikes(listX)
cntY,spikesY=countSpikes(listY)
Z=np.array(spikecorr)
Z=np.array([Z, Z, Z, Z, Z, Z, Z, Z, Z, Z])
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
linewidth=0, antialiased=False)
ax.set_zlim3d(np.argmin(Z), np.argmax(Z))
ax.w_zaxis.set_major_locator(LinearLocator(10))
ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.03f'))
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
'ENDSIM':2, 'FSV':10000, 'BRAINNAME':"vb-exp1", 'TAU_NOISE':0.020} #0.020 # parameters['CONN_INTERNAL']=i/100 # parameters['CONN_LATERAL']=i/100 parameters['ESYN_MAXCONDUCTANCE']=esynval parameters['ISYN_MAXCONDUCTANCE']=isynval esynval=esynval+0.01 isynval=isynval-0.1 # parameters['TAU_NOISE']=(trials-i)/trials parameters['BRAINNAME']='vb-exp'+str(int(ii)) brainsim.simBrain(parameters) listX = brainsim.loadBrain(parameters['BRAINNAME']+'-XReport.txt') listY = brainsim.loadBrain(parameters['BRAINNAME']+'-YReport.txt') nsamples=len(listX) t = numpy.linspace(0.0, parameters['ENDSIM'], nsamples, endpoint=False) A=numpy.array(listX).astype(float) B=numpy.array(listY).astype(float) # Purely spike correlation cntX,spikesX = countSpikes(listX) cntY,spikesY = countSpikes(listY) print "cntX,spikesX: ", cntX, spikesX print "cntY,spikesY: ", cntY, spikesY sxcorr = scipy.correlate(spikesX, spikesY) spikecorr.append(sxcorr[sxcorr.argmax()])
def loadExperimentXYI (prefix, esynval, isynval): listX = brainsim.loadBrain (prefix + str (esynval) + "-" + str(isynval) + "-XReport.txt") listY = brainsim.loadBrain (prefix + str (esynval) + "-" + str(isynval) + "-YReport.txt") cntX, spikesX = countSpikes (listX) cntY, spikesY = countSpikes (listY) return (listX,cntX,spikesX),(listY,cntY,spikesY)
lS=brainsim.loadBrain('vb-exp-SReport.txt')
lI=brainsim.loadBrain('vb-exp-IReport.txt')
plt.subplot(511)
plt.plot(sp,lB,'b')
plt.subplot(512)
plt.plot(sp,lG,'b')
plt.subplot(513)
plt.plot(sp,lT,'b')
plt.subplot(514)
plt.plot(sp,lS,'b')
plt.subplot(515)
plt.plot(sp,lI,'b')
plt.show()
brainsim.simBrain(parameters)
lB=brainsim.loadBrain('vb-exp-BReport.txt')
lG=brainsim.loadBrain('vb-exp-GReport.txt')
lT=brainsim.loadBrain('vb-exp-TReport.txt')
lS=brainsim.loadBrain('vb-exp-SReport.txt')
_,spikesB=brainalyze.countSpikes(lB,10000*(parameters['ENDSIM']/2))
_,spikesG=brainalyze.countSpikes(lG,10000*(parameters['ENDSIM']/2))
_,spikesT=brainalyze.countSpikes(lT,10000*(parameters['ENDSIM']/2))
_,spikesS=brainalyze.countSpikes(lS,10000*(parameters['ENDSIM']/2))
cBT=brainalyze.corr(spikesB,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cGT=brainalyze.corr(spikesG,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cBS=brainalyze.corr(spikesB,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
cGS=brainalyze.corr(spikesG,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
#cBS=cBS+10
#cGS=cGS+10
#cBS=(cBS/cGS)*cBT
#while x < len(rates):
# while y < len(rates):
# parameters['RATE1']=rates[x]
# parameters['RATE2']=rates[y]
# parameters['BRAINNAME']='vb-exp'+str(rates[x])+'-'+str(rates[y])
# brainsim.simBrain(parameters)
# y=y+1
# y=0 #reset
# x=x+1
x=0
y=0
while x < len(rates):
while y < len(rates):
print rates[x],rates[y]
lB=brainsim.loadBrain('vb-exp'+str(rates[x])+'-'+str(rates[y])+'-BReport.txt')
lG=brainsim.loadBrain('vb-exp'+str(rates[x])+'-'+str(rates[y])+'-GReport.txt')
lT=brainsim.loadBrain('vb-exp'+str(rates[x])+'-'+str(rates[y])+'-TReport.txt')
lS=brainsim.loadBrain('vb-exp'+str(rates[x])+'-'+str(rates[y])+'-SReport.txt')
_,spikesB=brainalyze.countSpikes(lB,10000*(parameters['ENDSIM']/2))
_,spikesG=brainalyze.countSpikes(lG,10000*(parameters['ENDSIM']/2))
_,spikesT=brainalyze.countSpikes(lT,10000*(parameters['ENDSIM']/2))
_,spikesS=brainalyze.countSpikes(lS,10000*(parameters['ENDSIM']/2))
cBT=brainalyze.corr(spikesB,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cGT=brainalyze.corr(spikesG,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cBS=brainalyze.corr(spikesB,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
cGS=brainalyze.corr(spikesG,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
# Blue Triangle and Green Square
if cBT > cGT and cGS > cBS:
store[x][y]=(abs(cBT - cGT) + abs(cGS - cBS))/2
else:
def loadExperiment (prefix, exp): listX = brainsim.loadBrain (prefix + str (exp) + "-XReport.txt") listY = brainsim.loadBrain (prefix + str (exp) + "-YReport.txt") cntX, spikesX = countSpikes (listX) cntY, spikesY = countSpikes (listY) return (listX,cntX,spikesX),(listY,cntY,spikesY)
