def run_simulations(): isynval=isynval_start cnt=0 while isynval < isynval_end: parameters['ISYN_MAXCONDUCTANCE']=isynval parameters['BRAINNAME']='vb-exp'+str(cnt) brainsim.simBrain(parameters) isynval=isynval+isynval_step cnt=cnt+1 return True
def run_simulations(): global esynval global esynval_step runs=0 while runs < trials: parameters['ESYN_MAXCONDUCTANCE']=esynval parameters['ISYN_MAXCONDUCTANCE']=isynval parameters['BRAINNAME']='vb-exp'+str(int(runs)) brainsim.simBrain(parameters) esynval=esynval+esynval_step runs=runs+1 return True
def run_simulations(): esynval=esynval_start isynval=isynval_start x=0 y=0 while x < max_x: while y < max_y: parameters['ESYN_MAXCONDUCTANCE']=esynval parameters['ISYN_MAXCONDUCTANCE']=isynval parameters['BRAINNAME']='vb-exp'+str(x)+'-'+str(y) # Resulting reports: # vb-exp-[x]-[y]-BReport.txt # vb-exp-[x]-[y]-GReport.txt # vb-exp-[x]-[y]-TReport.txt # vb-exp-[x]-[y]-SReport.txt brainsim.simBrain(parameters) isynval=isynval+isynval_step y=y+1 y=0 #reset isynval=isynval_start #reset esynval=esynval+esynval_step x=x+1
'CONN_INTERNAL':1, #0.02 '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()])
