import matplotlib matplotlib.use('Agg') import numpy from pylab import * import mytools import pickle import sys import scipy.io from os.path import exists f,axarr = subplots(1,3) for i in range(0,3): axarr[i].set_position([0.1+0.3*i,0.5,0.23,0.45]) f.text(0.065+0.3*i, 0.94, chr(ord('D')+i), fontsize=22) #Is = unique([0.34+0.0025*x for x in range(0,11)]+[0.35+0.05*x for x in range(0,22)]) Is = [0.69+0.005*x for x in range(0,43)] styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-'] cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00'] col_control = '#2222ff' ispDef = 0 # Consider a local maximum above -35mV a spike only if after last spike the membrane potential came below -45mV icell = 0 icomb = 0 if len(sys.argv) > 1: icomb = int(sys.argv[1]) combs_all = [ [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]], [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,3,0]], [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]], [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]], [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,4,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,1,0]], [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]], [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,3,0]], [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,0,0]], [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]] ] lensToStart = [150.0, 300.0, 450.0, 600.0, 650.0] startdist = int(lensToStart[2]) currCoeff = 1.1 unpicklefile = open('ifcurvesmut2_cs'+str(icell)+'_comb'+str(icomb)+'.sav', 'r') unpickledlist = pickle.load(unpicklefile) unpicklefile.close() ISIsThisMutVal = unpickledlist[0] spTimesThisMutVal = unpickledlist[1+ispDef] #Is_control = [0.35+0.05*x for x in range(0,22)] Is_control = [0.69+0.005*x for x in range(0,43)] unpicklefile = open('ifcurvesmut2_cs'+str(icell)+'_0_0_0.sav', 'r') unpickledlist = pickle.load(unpicklefile) unpicklefile.close() spTimesThisMutVal0 = unpickledlist[1+ispDef] nSpikes_control = [sum([1 for x in spTimesThisMutVal0[5][j] if x >= 500]) for j in range(0,len(Is_control))] somaticIs = [-0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.1, 0.15, 0.2] #synconductances = unique([0.000005, 0.00001, 0.000015, 0.00002, 0.000025, 0.00003, 0.000035, 0.00004, 0.000045, 0.00005, 0.000055, 0.0000025, 0.0000075, 0.0000125, 0.0000175, 0.0000225, 0.0000275, 0.0000325, 0.0000375, 0.0000425, 0.0000475, 0.0000525]) synconductances = array([0.00001, 0.00002, 0.00003, 0.00004, 0.00005, 0.00006, 0.00007, 0.00008, 0.00009, 0.0001]) thrs = [[0.08, 0.18, inf], [0.08, 0.18, inf], [0.08, 0.18, inf], [0.08, 0.18, inf], [0.08, 0.18, inf], [0.08, 0.18, inf], [0.08, 0.18, inf], [0,1,2,3,inf]] iters = [0, 2, 5, 6, 8] for iiter in range(0,len(iters)): iter = iters[iiter] if iter==5: continue nSpikes = [sum([1 for x in spTimesThisMutVal[iiter][j] if x >= 500]) for j in range(0,len(Is))] axarr[0].plot(Is, [x/15.5 for x in nSpikes], styles[iter],color=cols[iter],linewidth=1) axarr[0].plot(Is_control, [x/15.5 for x in nSpikes_control], styles[iter],color=col_control,linewidth=1) if exists('PPIcoeffs450_cs'+str(icell)+'_0.sav'): print ' opening PPIcoeffs450_cs'+str(icell)+'_0.sav and PPIcoeffs_complement_450_cs'+str(icell)+'_0.sav' unpicklefile = open('PPIcoeffs450_cs'+str(icell)+'_0.sav', 'r') unpickledlist = pickle.load(unpicklefile) unpicklefile.close() PPIcoeffs_control_Almog = unpickledlist[1][4] print str(unpickledlist[1][4]) ISIs_control_Almog = unpickledlist[2] else: print ' PPIcoeffs450_cs'+str(icell)+'_0.sav not found' ISIs_Almog = [10.0*x for x in range(0,51)] iters = [0, 2, 6, 8, -1] for iiter in range(0,len(iters)): iter = iters[iiter] if iter==5: continue unpicklefile = open('PPIcoeffs'+str(startdist)+'_cs'+str(icell)+'_comb'+str(icomb)+'_iter'+str(iter)+'.sav', 'r') unpickledlist = pickle.load(unpicklefile) unpicklefile.close() PPIcoeffs_Almog_thisiter = [x[:] for x in unpickledlist[1][0]] #ISIs_Almog = unpickledlist[2][:] if iter >= 0: axarr[1].plot(ISIs_Almog,[x[2]*currCoeff for x in PPIcoeffs_Almog_thisiter],color=cols[iter],linewidth=1.0) axarr[1].plot(ISIs_control_Almog,[x[2]*currCoeff for x in PPIcoeffs_control_Almog],color=col_control,linewidth=1.0) iters = [0, 2, 6, 8, -1] coding_outputs_thismut = [] coding_outputs_control = [] for isynconductance in range(0,len(synconductances)): coding_outputs_thiscond = [] coding_outputs_control_thiscond = [] synconductance = synconductances[isynconductance] for iI in range(0,len(somaticIs)): unpicklefile = open('codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'_somaticI'+str(somaticIs[iI])+'.sav', 'r') #maybe codings/codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'.sav' unpickledlist = pickle.load(unpicklefile) unpicklefile.close() coding_outputs = unpickledlist[2] myfigs = [[],[],[],[],[]] myfigs_control = [] for iplot in range(0,8): for iiter in range(0,len(iters)): iter = iters[iiter] if iter == -1: myfigs_control.append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)]) myfigs[iiter].append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)]) coding_outputs_thiscond.append(myfigs[:]) if iter == -1: coding_outputs_control_thiscond.append(myfigs_control[:]) coding_outputs_thismut.append(coding_outputs_thiscond[:]) if iter == -1: coding_outputs_control.append(coding_outputs_control_thiscond[:]) print "Analyzing controls..." Npatterns_control = [] for icond in range(0,len(synconductances)): Npatterns_control.append(mean([len(unique([sum([(4**iplot)*coding_outputs_control[icond][iI][iplot][j] for iplot in range(0,8)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))])) for iiter in range(0,len(iters)): iter = iters[iiter] if iter == 5: continue Npatterns = [] for icond in range(0,len(synconductances)): Npatterns.append(mean([len(unique([sum([(4**iplot)*coding_outputs_thismut[icond][iI][iiter][iplot][j] for iplot in range(0,8)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))])) #print str(Npatterns) print "iiter="+str(iiter)+", Npatterns="+str(mean(Npatterns))+" +- "+str(std(Npatterns)) if iter >= 0: axarr[2].plot(1e6*synconductances,Npatterns,'b-',color=cols[iter]) else: axarr[2].plot(1e6*synconductances,Npatterns,'k--',dashes=(1,4)) axarr[2].plot(1e6*synconductances,Npatterns_control,'b-',color=col_control) axarr[0].set_xlabel('\$I\$ (nA)',fontsize=9) #axarr[0].set_xticks([0.4,0.8,1.2]) axarr[0].set_ylabel('\$f\$ (Hz)',fontsize=9) axarr[0].set_yticks([0,10,20]) axarr[0].set_xlim([0.7,0.9]) axarr[0].set_ylim([0,25]) axarr[1].set_xlabel('ISI (ms)',fontsize=9) #axarr[1].set_xticks([0,100,200,300,400,500]) axarr[1].set_ylabel('PPI factor',fontsize=9) axarr[1].set_yticks([0.75,1.0,1.25]) axarr[1].set_xlim([0,300]) axarr[1].set_ylim([0.6,1.4]) axarr[2].set_xlabel('Single-synapse conductance (pS)',fontsize=9) axarr[2].set_xticks([20,40,60,80,100]) axarr[2].set_ylabel('output diversity',fontsize=9) axarr[2].set_yticks([10,20,30]) axarr[2].set_ylim([10,39]) for i in range(0,3): for tick in axarr[i].yaxis.get_major_ticks()+axarr[i].xaxis.get_major_ticks(): tick.label.set_fontsize(6) #f.savefig("figcomb"+str(icomb)+".eps") f.savefig("figcomb.eps")