def plot_NMDAspike(self,remakeFig=False):
with open(self.datapath+'NMDAspike_latest.pkl','rb') as f:
res = pickle.load(f)
fig = MyFigure(figsize=(1.4,0.8))
pd=fig.addplot([0.3,0.4,0.65,0.55])
for j in xrange(len(res['vDend_list'])):
plot(res['times'],res['vDend_list'][j],color='black')
pd.xlabel('Time (ms)',labelpad=3)
pd.ylabel('$V_D$ (mV)',labelpad=2)
pd.ax.set_yticks([-70,-20])
pd.ax.set_xticks([0,100])
pd.xaxis.set_tick_params(pad=0.)
pd.yaxis.set_tick_params(pad=0.)
if remakeFig:
fig.save(self.figpath+'plot_NMDAspike_Dend'+self.version)
fig = MyFigure(figsize=(1.4,0.7))
pd=fig.addplot([0.3,0.3,0.65,0.65])
for j in xrange(len(res['vDend_list'])):
plot(res['times'],res['vSoma_list'][j],color='black')
pd.xlabel('Time (ms)',labelpad=3)
ylabel('$V_S$ (mV)',labelpad=0)
pd.ax.set_yticks([-70,-60])
pd.ax.set_xticks([0,100])
pd.xaxis.set_tick_params(pad=0.)
pd.yaxis.set_tick_params(pad=0.)
if remakeFig:
fig.save(self.figpath+'plot_NMDAspike_Soma'+self.version)
show()
def plot_NMDAspike(self, remakeFig=False):
with open(self.datapath + 'NMDAspike_latest.pkl', 'rb') as f:
res = pickle.load(f)
fig = MyFigure(figsize=(1.4, 0.8))
pd = fig.addplot([0.3, 0.4, 0.65, 0.55])
for j in xrange(len(res['vDend_list'])):
plot(res['times'], res['vDend_list'][j], color='black')
pd.xlabel('Time (ms)', labelpad=3)
pd.ylabel('$V_D$ (mV)', labelpad=2)
pd.ax.set_yticks([-70, -20])
pd.ax.set_xticks([0, 100])
pd.xaxis.set_tick_params(pad=0.)
pd.yaxis.set_tick_params(pad=0.)
if remakeFig:
fig.save(self.figpath + 'plot_NMDAspike_Dend' + self.version)
fig = MyFigure(figsize=(1.4, 0.7))
pd = fig.addplot([0.3, 0.3, 0.65, 0.65])
for j in xrange(len(res['vDend_list'])):
plot(res['times'], res['vSoma_list'][j], color='black')
pd.xlabel('Time (ms)', labelpad=3)
ylabel('$V_S$ (mV)', labelpad=0)
pd.ax.set_yticks([-70, -60])
pd.ax.set_xticks([0, 100])
pd.xaxis.set_tick_params(pad=0.)
pd.yaxis.set_tick_params(pad=0.)
if remakeFig:
fig.save(self.figpath + 'plot_NMDAspike_Soma' + self.version)
show()
def plot_NevianFit(self):
with open(self.datapath+'learning_para_2014-11-10_0','rb') as f:
para = pickle.load(f)
experiment3B = 'NevianFig3B'
with open(self.datapath+'resultlist_'+experiment3B,'rb') as f:
result_list3B = pickle.load(f)
experiment3D_full = 'NevianFig3D_full'
with open(self.datapath+'resultlist_'+experiment3D_full,'rb') as f:
result_list3D_full = pickle.load(f)
experiment2_full = 'NevianFig2_full'
with open(self.datapath+'resultlist_'+experiment2_full,'rb') as f:
result_list2_full = pickle.load(f)
remakeFig = True
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.3,0.25,0.65,0.7])
self.plot_model_result(pl, para, result_list2_full, experiment2_full, plot_data=True)
#fig.save(self.figpath+'Fit'+experiment2_full+'_'+self.version)
#compareWithNevian_CaTrace(para,remakeFig,experiment = 'NevianFig5')
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.3,0.25,0.65,0.7])
self.plot_model_result(pl, para, result_list3B, experiment3B, plot_data=True)
#fig.save(self.figpath+'Fit'+experiment3B+'_'+self.version)
#compareWithNevian_CaTrace(para,remakeFig,experiment = experiment3B)
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.3,0.25,0.65,0.7])
self.plot_model_result(pl, para, result_list3D_full, experiment3D_full, plot_data=True)
fig.save(self.figpath+'Fit'+experiment3D_full+'_'+self.version)
def plot_NMDAPlateauMechanism(self):
p = MCM.read_params(self.paramsfile)
# NMDA channel voltage-glutamate input relationship
betaPlot = linspace(0, 10, 100)
def vSteadyFunc(p, v, beta):
gL = 1
gNMDA = beta * gL
#gGABA = 1
iLeak = -gL * (v - p['vRest_pyr'])
iNMDA = -gNMDA * (v - p['vE_pyr']) / (
1 + exp(-(v - p['vHalfNMDA']) / p['vSpreadNMDA']))
iGABA = -gGABA * (v - p['vI_pyr'])
iTot = iLeak + iNMDA + iGABA
return iTot
gGABA = 0
figure()
vSteadyPlot_low = list()
vSteadyPlot_high = list()
for beta in betaPlot:
sol = sp.optimize.newton(lambda v: vSteadyFunc(p, v, beta),
-70 * mvolt,
maxiter=1000)
vSteadyPlot_low.append(sol)
sol = sp.optimize.newton(lambda v: vSteadyFunc(p, v, beta),
0 * mvolt,
maxiter=1000)
vSteadyPlot_high.append(sol)
theta = betaPlot[array(vSteadyPlot_high) / mvolt < -40][-1]
fig = MyFigure(figsize=(1.5, 1.3))
pl = fig.addplot([0.35, 0.3, 0.6, 0.65])
pl.plot([theta] * 2, [-70, 0],
'--',
color=array([189, 189, 189]) / 255)
pl.plot(betaPlot, array(vSteadyPlot_low) / mvolt, color='black')
pl.plot(betaPlot, array(vSteadyPlot_high) / mvolt, color='black')
pl.ax.set_yticks([-70, -10])
pl.ax.set_xticks([0, theta, 10])
pl.ax.set_xticklabels(['0', r'$\theta_{\mathrm{NMDA}}$', '10'])
xlabel('$g_{\mathrm{NMDA}}/(g_L+g_{\mathrm{GABA}})$')
ylabel('$V_{ss}$ (mV)', labelpad=-5)
fig.save(self.figpath + 'plot_NMDAPlateauMechanism' + self.version)
vplot = linspace(-70, -15, 100) * mV
fMg = 1 / (1 + exp(-(vplot - p['vHalfNMDA']) / p['vSpreadNMDA']))
fig = MyFigure(figsize=(1.5, 1.3))
pl = fig.addplot([0.35, 0.3, 0.6, 0.65])
pl.plot(vplot / mV, fMg, color='black')
ylabel('$f_{\mathrm{Mg}}(V)$', labelpad=-5)
xlabel('$V_D$ (mV)')
pl.ax.set_xticks([-70, -10])
pl.ax.set_yticks([0, 0.5])
def plot_NMDAPlateauMechanism(self):
p = MCM.read_params(self.paramsfile)
# NMDA channel voltage-glutamate input relationship
betaPlot = linspace(0,10,100)
def vSteadyFunc(p,v,beta):
gL = 1
gNMDA = beta*gL
#gGABA = 1
iLeak = -gL*(v-p['vRest_pyr'])
iNMDA = -gNMDA*(v-p['vE_pyr'])/(1+exp(-(v-p['vHalfNMDA'])/p['vSpreadNMDA']))
iGABA = -gGABA*(v-p['vI_pyr'])
iTot = iLeak + iNMDA + iGABA
return iTot
gGABA=0
figure()
vSteadyPlot_low = list()
vSteadyPlot_high = list()
for beta in betaPlot:
sol = sp.optimize.newton(lambda v: vSteadyFunc(p,v,beta),-70*mvolt,maxiter=1000)
vSteadyPlot_low.append(sol)
sol = sp.optimize.newton(lambda v: vSteadyFunc(p,v,beta),0*mvolt,maxiter=1000)
vSteadyPlot_high.append(sol)
theta = betaPlot[array(vSteadyPlot_high)/mvolt<-40][-1]
fig = MyFigure(figsize=(1.5,1.3))
pl=fig.addplot([0.35,0.3,0.6,0.65])
pl.plot([theta]*2,[-70,0],'--',color=array([189,189,189])/255)
pl.plot(betaPlot,array(vSteadyPlot_low)/mvolt, color='black')
pl.plot(betaPlot,array(vSteadyPlot_high)/mvolt, color='black')
pl.ax.set_yticks([-70,-10])
pl.ax.set_xticks([0,theta,10])
pl.ax.set_xticklabels(['0',r'$\theta_{\mathrm{NMDA}}$','10'])
xlabel('$g_{\mathrm{NMDA}}/(g_L+g_{\mathrm{GABA}})$')
ylabel('$V_{ss}$ (mV)',labelpad=-5)
fig.save(self.figpath+'plot_NMDAPlateauMechanism'+self.version)
vplot = linspace(-70,-15,100)*mV
fMg = 1/(1+exp(-(vplot-p['vHalfNMDA'])/p['vSpreadNMDA']))
fig = MyFigure(figsize=(1.5,1.3))
pl=fig.addplot([0.35,0.3,0.6,0.65])
pl.plot(vplot/mV,fMg, color='black')
ylabel('$f_{\mathrm{Mg}}(V)$',labelpad=-5)
xlabel('$V_D$ (mV)')
pl.ax.set_xticks([-70,-10])
pl.ax.set_yticks([0,0.5])
def plot_Newsome_statespace_legend(self):
i_dim = 0
n_m = 6
for colors in [motion_colors, color_colors]:
mks = 4
fig = MyFigure(figsize=(0.75, 0.2))
pl = fig.addplot(rect=[0.05, 0.05, 0.9, 0.9])
for i_coh in xrange(n_m):
if i_coh < (n_m / 2):
mfc = 'white'
mec = colors[i_coh]
else:
mfc = colors[n_m - i_coh - 1]
mec = mfc
pl.plot(i_coh,
0,
'o',
markerfacecolor=mfc,
markeredgecolor=mec,
markersize=mks)
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
fig.save(self.figpath +
'plot_Newsome_statespace_legend_%d' % i_dim)
i_dim += 1
def plot_WeightChangevsRate(self):
with open(self.datapath + 'calc_WeightChangevsRate', 'rb') as f:
output = pickle.load(f)
p = output['params']
w_post_list = output['w_post_list']
dend_inh_rates = p['dend_inh_rates']
colorMatrix = array([[65, 182, 196], [34, 94, 168], [8, 29, 88]]) / 255
fig = MyFigure(figsize=(2.5, 1.2))
pl = fig.addplot(rect=[0.25, 0.3, 0.7, 0.65])
pl.plot([0, max(p['pre_rates'])], [1, 1],
color=array([189, 189, 189]) / 255)
i = 0
plotlist = list()
for w_post in w_post_list:
ax, = pl.plot(p['pre_rates'], w_post, color=colorMatrix[i, :])
plotlist.append(ax)
plt.xlabel('Pre-synaptic rate (Hz)')
plt.ylabel('Weight change')
i += 1
pl.ax.set_xticks([0, 20, 40])
pl.ax.set_plt.ylim((0, 3))
pl.ax.set_yticks([0, 1, 2, 3])
leg = plt.legend(plotlist, ['%d' % r for r in dend_inh_rates],
title='Inhibition (Hz)',
loc=2,
bbox_to_anchor=[0.1, 1.15])
leg.draw_frame(False)
fig.save(self.figpath + 'WeightChangevsRate' + self.version)
def plot_basic_pathway_gating_slice(self,mode,plot_ylabel=True):
with open(self.datapath+'basic_pathway_gating_response_slice_'+mode+'_latest','rb') as f:
p = pickle.load(f)
#print p['firing_rate_plot']
fig=MyFigure(figsize=(1.8,0.75))
pl = fig.addplot(rect=[0.25,0.15,0.55,0.65])
#pl.plot(p['firing_rate_plot'][:p['np']],color=colorMatrix[1],label='%d' % p['bkg_inh_rate'])
#pl.plot(p['firing_rate_plot'][p['np']:],color=colorMatrix[5],label='%d' % (p['bkg_inh_rate']+p['dend_inh_rate']))
pl.plot(p['firing_rate_plot'][:p['np']],color=colorMatrix[1],label='1')
pl.plot(p['firing_rate_plot'][p['np']:],color=colorMatrix[5],label='2')
#xlabel('Pathway 1 stimulus')
#xticks([p['np']//2],['preferred'])
xticks([p['np']//2],[''])
if mode in ['prelearning','postlearning']:
maxr = 25
else:
maxr = p['firing_rate_plot'].max()
pl.ax.set_ylim((0,maxr))
pl.ax.set_yticks([0,maxr//5*5])
if plot_ylabel:
ylabel('Output rate (Hz)')
leg = legend(title='Open gate',loc=1, bbox_to_anchor=[1.0, 1.35], frameon=False)
else:
pl.ax.set_yticklabels(['',''])
fig.save(self.figpath+'basic_pathway_gating_slice_'+mode+self.version)
ron = np.max(p['firing_rate_plot'][:p['np']]) - np.min(p['firing_rate_plot'][:])
roff = np.max(p['firing_rate_plot'][p['np']:]) - np.min(p['firing_rate_plot'][:])
print 'Gating selectivity is %0.2f' % ((ron-roff)/(ron+roff))
def plot_sNMDAmean(self):
p = MCM.read_params(self.paramsfile)
def meansNMDA(rate,p):
x_mean = rate*p['tauNMDARise']
temp = p['alphaNMDA']*p['tauNMDADecay']*x_mean
s_mean = temp/(1+temp)
return s_mean
theta = 0.645 # make threshold around 30 Hz
#theta = 0.6784
gGABA = 0.6
# NMDA channel voltage-glutamate input relationship
fig = MyFigure(figsize=(1.9,1.5))
pl=fig.addplot([0.4,0.3,0.5,0.65])
rates = linspace(0,300,100)
meanNMDAplot=meansNMDA(rates,p)
meanNMDAplot2 = meanNMDAplot/(1+gGABA)
pl.plot([0,300],[theta]*2,'--',color=array([189,189,189])/255)
pl.plot(rates,meanNMDAplot,color=colorMatrix[1],label='0')
pl.plot(rates,meanNMDAplot2,color=colorMatrix[5],label='%0.1f' % gGABA)
xlabel('NMDA input rate (Hz)')
ylabel('$g_{\mathrm{NMDA}}/(g_L+g_{\mathrm{GABA}})$')
pl.ax.set_yticks([0,theta])
pl.ax.set_yticklabels(['0',r'$\theta_{\mathrm{NMDA}}$'])
pl.ax.set_xticks([0,150,300])
leg=legend(title='$g_{\mathrm{GABA}}/g_L$',loc=4,bbox_to_anchor=[1.2, -0.05])
leg.draw_frame(False)
fig.save(self.figpath+'sNMDAmean'+self.version)
def plot_WeightChangevsRate(self):
with open(self.datapath+'calc_WeightChangevsRate','rb') as f:
output = pickle.load(f)
p = output['params']
w_post_list = output['w_post_list']
dend_inh_rates = p['dend_inh_rates']
colorMatrix = array([[65,182,196],
[34,94,168],
[8,29,88]])/255
fig=MyFigure(figsize=(2.5,1.2))
pl = fig.addplot(rect=[0.25,0.3,0.7,0.65])
pl.plot([0,max(p['pre_rates'])],[1,1],color=array([189,189,189])/255)
i = 0
plotlist = list()
for w_post in w_post_list:
ax, = pl.plot(p['pre_rates'],w_post,color=colorMatrix[i,:])
plotlist.append(ax)
plt.xlabel('Pre-synaptic rate (Hz)')
plt.ylabel('Weight change')
i += 1
pl.ax.set_xticks([0,20,40])
pl.ax.set_plt.ylim((0,3))
pl.ax.set_yticks([0,1,2,3])
leg=plt.legend(plotlist,['%d' % r for r in dend_inh_rates],title='Inhibition (Hz)',
loc=2,bbox_to_anchor=[0.1, 1.15])
leg.draw_frame(False)
fig.save(self.figpath+'WeightChangevsRate'+self.version)
def fit_RatevsI(self):
'''
fit the somatic firing rate as a function of current injection
'''
with open(self.datapath+'RatevsI'+self.version,'rb') as f:
result = pickle.load(f)
x0 = [ 174.85951028, 45.16030095, 2.89003711]
bnds = ((100,500),(10,1000),(0,5))
res = scipy.optimize.minimize(lambda p: self.obj_func_RatevsI(p,result),
x0,bounds=bnds,method='SLSQP',
options={'maxiter':100, 'disp':True})
print res.x
p = res.x
xdata = result['clamped_current_list']
ydata = result['fr_list']
ymodel, _ = self.obj_func_RatevsI(p,result,rich_return=True)
fig=MyFigure(figsize=(2,1.5))
pl = fig.addplot(rect=[0.25,0.25,0.7,0.7])
pl.plot(xdata,ydata,color=np.array([55,126,184])/255)
pl.plot(xdata,ymodel,color='black')
pl.ax.set_yticks([0,50,100])
pl.xlabel(r'$I$ (pA)')
pl.ylabel('Firing rate (Hz)')
leg = pl.legend(['Simulation','Fit'],loc=2)
leg.draw_frame(False)
fig.save(self.figpath+'RatevsI'+self.version)
def plot_fit_vary_param(self):
with open(self.datapath+'fit_vary_param'+ self.version,'rb') as f:
res = pickle.load(f)
n_som2dend_list = res.keys()
n_som2dend_list.remove('best_sqe')
n_som2dend_list.remove('worst_sqe')
n_som2dend_list.remove('monkey')
sqe_list = list()
for n_som2dend in n_som2dend_list:
sqes = res[n_som2dend].values()
sqe_list.append(np.median(sqes))
fig=MyFigure(figsize=(2,1.7))
pl = fig.addplot(rect=[0.3,0.3,0.65,0.65])
pl.plot(n_som2dend_list,sqe_list,
'o-',markeredgecolor='black',markerfacecolor='black',markersize=2,
color=np.array([150,150,150])/255,linewidth=1.5,label='model')
pl.plot(n_som2dend_list,[res['best_sqe']]*len(n_som2dend_list),
'-',markeredgecolor='black',markerfacecolor='black',
color=np.array([150,150,150])/255,linewidth=1.5,label='optimal')
pylab.ylabel('Sum of squared errors')
pylab.xlabel(r'$N_{\mathit{SOM}\rightarrow dend}$')
#pl.ax.set_yticks([0.01,0.02,0.03])
pl.ax.set_yticks([0,0.05,0.1])
pl.ax.set_xticks([5,10,15,20])
pylab.legend(loc=2,numpoints=1,frameon=False)
#pl.plot(n_som2dend_list,[res['worst_sqe']]*len(n_som2dend_list),'--',markeredgecolor='black',markerfacecolor='black',linewidth=1.5)
fig.save(self.figpath+ 'NewsomeFit_vary_param' + self.version)
def plot_NevianFit(self):
with open(self.datapath + 'learning_para_2014-11-10_0', 'rb') as f:
para = pickle.load(f)
experiment3B = 'NevianFig3B'
with open(self.datapath + 'resultlist_' + experiment3B, 'rb') as f:
result_list3B = pickle.load(f)
experiment3D_full = 'NevianFig3D_full'
with open(self.datapath + 'resultlist_' + experiment3D_full,
'rb') as f:
result_list3D_full = pickle.load(f)
experiment2_full = 'NevianFig2_full'
with open(self.datapath + 'resultlist_' + experiment2_full, 'rb') as f:
result_list2_full = pickle.load(f)
remakeFig = True
fig = MyFigure(figsize=(1.5, 1.5))
pl = fig.addplot(rect=[0.3, 0.25, 0.65, 0.7])
self.plot_model_result(pl,
para,
result_list2_full,
experiment2_full,
plot_data=True)
#fig.save(self.figpath+'Fit'+experiment2_full+'_'+self.version)
#compareWithNevian_CaTrace(para,remakeFig,experiment = 'NevianFig5')
fig = MyFigure(figsize=(1.5, 1.5))
pl = fig.addplot(rect=[0.3, 0.25, 0.65, 0.7])
self.plot_model_result(pl,
para,
result_list3B,
experiment3B,
plot_data=True)
#fig.save(self.figpath+'Fit'+experiment3B+'_'+self.version)
#compareWithNevian_CaTrace(para,remakeFig,experiment = experiment3B)
fig = MyFigure(figsize=(1.5, 1.5))
pl = fig.addplot(rect=[0.3, 0.25, 0.65, 0.7])
self.plot_model_result(pl,
para,
result_list3D_full,
experiment3D_full,
plot_data=True)
fig.save(self.figpath + 'Fit' + experiment3D_full + '_' + self.version)
def fit_DendVvsgErI(self):
'''
fit the dendritic voltage as a function of excitatory input and inhibitory rate
'''
with open(self.datapath+'DendVvsgErI'+self.version,'rb') as f:
result = pickle.load(f)
x0 = [5,10,7,1]
bnds = ((1,20),(1,20),(1,20),(0,3))
params = copy.copy(self.params)
for key in result['params']:
params[key] = result['params'][key] # overwrite the old value
temp = params['pre_rate']*params['tauNMDARise']*params['tauNMDADecay']*params['alphaNMDA']
print params['tauNMDARise']*params['tauNMDADecay']*params['alphaNMDA']
# 0.06 is obtained from tau_rise = 2ms, tau_decay=100ms, alpha=0.3 /ms
s_mean = temp/(1+temp) # mean gating variable
result['Exc'] = result['weights']*params['gNMDA']*1e9*params['num_input']*s_mean
# unit-less weights * synaptic conductance 2.5nS * 15 synapses * mean gating variable for 30Hz
result['Inh'] = result['dend_inh_rates'] *params['tauGABA']*params['gGABA']*1e9
# Total Rate * synaptic conductance * tau_GABA
res = scipy.optimize.minimize(lambda p: self.obj_func_DendVvsgErI(p,result),
x0,bounds=bnds,method='SLSQP',
options={'maxiter':100, 'disp':True})
print res.x
p = res.x
xdata = result['Exc']
n_curve = len(result['vdend_mean_list'])
fig=MyFigure(figsize=(2,1.5))
pl = fig.addplot(rect=[0.25,0.25,0.7,0.7])
for i in xrange(n_curve):
ydata = result['vdend_mean_list'][i]*1000
ax_data, = pl.plot(xdata,ydata,color=np.array([55,126,184])/255)
ymodel_list, sqe = self.obj_func_DendVvsgErI(p,result,rich_return=True)
for i in xrange(n_curve):
ax_model, = pl.plot(xdata,ymodel_list[i],color='black')
pl.xlabel(r'$g_{E,\mathrm{tot}}$ (nS)',labelpad = 3)
pl.ylabel(r'$\overline{V}_D$ (mV)')
pl.ax.set_xlim((0,50))
pl.ax.set_xticks([0,25,50])
pl.ax.set_ylim((-70,-10))
pl.ax.set_yticks([-70,-20])
leg = pl.legend([ax_data,ax_model],['Simulation','Fit'],loc=2, bbox_to_anchor=[0, 1.05])
leg.draw_frame(False)
fig.save(self.figpath+'DendVvsgErI'+self.version)
def plot_voltagedistribution(self,denseinh = False):
'''
Plot voltage distribution
'''
p = dict()
p['num_DendEach']=1
p['dt'] = 0.2*ms
p['num_Soma'] = 40
p['pre_rate'] = 40*Hz
p['inh_base'] = 5*Hz
if denseinh:
p['gGABA'] = p['gGABA']/10
p['inh_base'] = p['inh_base']*10
self.version = 'denseinh_' + self.version
pre_rates = ones(p['num_Soma'])*p['pre_rate']
model = MCM.Model(paramsfile=self.paramsfile,eqsfile=self.eqsfile,outsidePara=p)
model.make_model_dendrite_only(num_soma = p['num_Soma'],clamped_somavolt=-60*mV,condition='invivo')
model.make_dend_bkginh(inh_rates=p['inh_base'])
model.single_pathway_gating_experiment(pre_rates, num_input=15,w_input = 1)
model.make_network()
model.reinit()
net = Network(model)
net.run(2.5*second,report='text')
mon = model.monitor
times = mon['MvDend'].times/ms
plotstart = 500
dendv = mon['MvDend'].values[:,times>plotstart]/mV
dendv = dendv[range(0,p['num_DendEach']*p['num_Soma'],p['num_DendEach']),:]
res = dict()
res['params'] = p
res['dendv'] = dendv
with open(self.datapath+'voltage_distribution_'+self.version,'wb') as f:
pickle.dump(res,f)
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.05,0.3,0.9,0.65])
#pl.ax.set_frame_on(False)
pl.ax.spines['left'].set_visible(False)
pl.ax.get_yaxis().set_visible(False)
n, bins, patches = hist(dendv.flatten(), 50, normed=1, histtype='stepfilled')
color_hist = array([99,99,99])/255
setp(patches, 'facecolor', color_hist,'edgecolor',color_hist)
xlim([-70,-10])
xticks([-60,-50,-40,-30,-20],['-60','','-40','','-20'])
xlabel('$V_D$ (mV)')
fig.save(self.figpath+'VoltageDistribution_'+self.version)
def plot_GABAABratio(self):
with open(self.datapath+'GatingCapacity_varyGABAABratio'+self.version,'rb') as f:
p = pickle.load(f)
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.3,0.3,0.65,0.65])
pl.plot(1-p['GABAA_prop_plot'],p['gs1_plot'],
'o-', color=colorMatrix1[1],
markeredgecolor = 'none', markersize=3)
print p['gs1_plot']
ylabel('Gating selectivity')
ylim((0,1))
xlim((0,1))
yticks([0,0.5,1],['0','0.5','1'])
xticks([0,0.5,1],['0','0.5','1'])
xlabel(r'$\mathrm{GABA}_{\mathrm{B}}$ conductance ratio')
fig.save(self.figpath+'GatingCapacity_varyGABAABratio'+self.version)
def plot_basic_pathway_gating_slice(self, mode, plot_ylabel=True):
with open(
self.datapath + 'basic_pathway_gating_response_slice_' + mode +
'_latest', 'rb') as f:
p = pickle.load(f)
#print p['firing_rate_plot']
fig = MyFigure(figsize=(1.8, 0.75))
pl = fig.addplot(rect=[0.25, 0.15, 0.55, 0.65])
#pl.plot(p['firing_rate_plot'][:p['np']],color=colorMatrix[1],label='%d' % p['bkg_inh_rate'])
#pl.plot(p['firing_rate_plot'][p['np']:],color=colorMatrix[5],label='%d' % (p['bkg_inh_rate']+p['dend_inh_rate']))
pl.plot(p['firing_rate_plot'][:p['np']],
color=colorMatrix[1],
label='1')
pl.plot(p['firing_rate_plot'][p['np']:],
color=colorMatrix[5],
label='2')
#xlabel('Pathway 1 stimulus')
#xticks([p['np']//2],['preferred'])
xticks([p['np'] // 2], [''])
if mode in ['prelearning', 'postlearning']:
maxr = 25
else:
maxr = p['firing_rate_plot'].max()
pl.ax.set_ylim((0, maxr))
pl.ax.set_yticks([0, maxr // 5 * 5])
if plot_ylabel:
ylabel('Output rate (Hz)')
leg = legend(title='Open gate',
loc=1,
bbox_to_anchor=[1.0, 1.35],
frameon=False)
else:
pl.ax.set_yticklabels(['', ''])
fig.save(self.figpath + 'basic_pathway_gating_slice_' + mode +
self.version)
ron = np.max(p['firing_rate_plot'][:p['np']]) - np.min(
p['firing_rate_plot'][:])
roff = np.max(p['firing_rate_plot'][p['np']:]) - np.min(
p['firing_rate_plot'][:])
print 'Gating selectivity is %0.2f' % ((ron - roff) / (ron + roff))
def plot_fit_vary_param(self):
with open(self.datapath + 'fit_vary_param' + self.version, 'rb') as f:
res = pickle.load(f)
n_som2dend_list = res.keys()
n_som2dend_list.remove('best_sqe')
n_som2dend_list.remove('worst_sqe')
n_som2dend_list.remove('monkey')
sqe_list = list()
for n_som2dend in n_som2dend_list:
sqes = res[n_som2dend].values()
sqe_list.append(np.median(sqes))
fig = MyFigure(figsize=(2, 1.7))
pl = fig.addplot(rect=[0.3, 0.3, 0.65, 0.65])
pl.plot(n_som2dend_list,
sqe_list,
'o-',
markeredgecolor='black',
markerfacecolor='black',
markersize=2,
color=np.array([150, 150, 150]) / 255,
linewidth=1.5,
label='model')
pl.plot(n_som2dend_list, [res['best_sqe']] * len(n_som2dend_list),
'-',
markeredgecolor='black',
markerfacecolor='black',
color=np.array([150, 150, 150]) / 255,
linewidth=1.5,
label='optimal')
pylab.ylabel('Sum of squared errors')
pylab.xlabel(r'$N_{\mathit{SOM}\rightarrow dend}$')
#pl.ax.set_yticks([0.01,0.02,0.03])
pl.ax.set_yticks([0, 0.05, 0.1])
pl.ax.set_xticks([5, 10, 15, 20])
pylab.legend(loc=2, numpoints=1, frameon=False)
#pl.plot(n_som2dend_list,[res['worst_sqe']]*len(n_som2dend_list),'--',markeredgecolor='black',markerfacecolor='black',linewidth=1.5)
fig.save(self.figpath + 'NewsomeFit_vary_param' + self.version)
def plot_DendVvsgErI(self):
with open(self.datapath+'DendVvsgErI'+self.version,'rb') as f:
res = pickle.load(f)
fig=MyFigure(figsize=(4,3))
pl = fig.addplot(rect=[0.2,0.15,0.7,0.8])
i = 0
for vdend_mean in res['vdend_mean_list']:
pl.plot(res['params']['weights'],vdend_mean/mV,
label='%d' % res['dend_inh_rates'][i])
xlabel('Weight')
ylabel('Mean Dendritic Voltage (mV)')
i += 1
pl.ax.set_xlim((0,2))
pl.ax.set_xticks([0,1,2])
pl.ax.set_ylim((-70,-10))
pl.ax.set_yticks([-70,-50,-30,-10])
leg=legend(title='Inhibition (Hz)',loc=2)
leg.draw_frame(False)
fig.save(self.figpath+'DendVvsgErI'+self.version)
def fit_RatevsDendV(self):
'''
fit the somatic firing rate as a function of mean dendritic voltage
'''
with open(self.datapath + 'RatevsDendV' + self.version, 'rb') as f:
result = pickle.load(f)
x0 = [1.5, 0.08, 2.2]
bnds = ((0, 5), (0, 1), (1, 3))
params = copy.copy(self.params)
for key in result['params']:
params[key] = result['params'][key] # overwrite the old value
res = scipy.optimize.minimize(
lambda p: self.obj_func_RatevsDendV(p, result),
x0,
bounds=bnds,
method='SLSQP',
options={
'maxiter': 100,
'disp': True
})
print res.x
p = res.x
xdata = result['clamped_dendvolt_list'] + 70
ydata = result['fr_list']
ymodel, _ = self.obj_func_RatevsDendV(p, result, rich_return=True)
fig = MyFigure(figsize=(2, 1.5))
pl = fig.addplot(rect=[0.25, 0.25, 0.7, 0.7])
pl.plot(xdata - 70, ydata, color=np.array([55, 126, 184]) / 255)
pl.plot(xdata - 70, ymodel, color='black')
pl.ax.set_xticks([-70, -60, -50, -40])
pl.ax.set_yticks([0, 50, 100, 150])
pl.xlabel(r'$\langle \overline{V}_D\rangle$ (mV)')
pl.ylabel('Firing rate (Hz)')
leg = pl.legend(['Simulation', 'Fit'], loc=2)
leg.draw_frame(False)
fig.save(self.figpath + 'RatevsDendV' + self.version)
def plot_DendVvsgErI(self):
with open(self.datapath + 'DendVvsgErI' + self.version, 'rb') as f:
res = pickle.load(f)
fig = MyFigure(figsize=(4, 3))
pl = fig.addplot(rect=[0.2, 0.15, 0.7, 0.8])
i = 0
for vdend_mean in res['vdend_mean_list']:
pl.plot(res['params']['weights'],
vdend_mean / mV,
label='%d' % res['dend_inh_rates'][i])
xlabel('Weight')
ylabel('Mean Dendritic Voltage (mV)')
i += 1
pl.ax.set_xlim((0, 2))
pl.ax.set_xticks([0, 1, 2])
pl.ax.set_ylim((-70, -10))
pl.ax.set_yticks([-70, -50, -30, -10])
leg = legend(title='Inhibition (Hz)', loc=2)
leg.draw_frame(False)
fig.save(self.figpath + 'DendVvsgErI' + self.version)
def plot_Newsome_statespace_legend(self):
i_dim = 0
n_m = 6
for colors in [motion_colors,color_colors]:
mks = 4
fig=MyFigure(figsize=(0.75,0.2))
pl = fig.addplot(rect=[0.05,0.05,0.9,0.9])
for i_coh in xrange(n_m):
if i_coh<(n_m/2):
mfc = 'white'
mec = colors[i_coh]
else:
mfc = colors[n_m-i_coh-1]
mec = mfc
pl.plot(i_coh,0,'o',markerfacecolor=mfc,markeredgecolor=mec,markersize=mks)
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
fig.save(self.figpath+'plot_Newsome_statespace_legend_%d' % i_dim)
i_dim += 1
def fit_RatevsI(self):
'''
fit the somatic firing rate as a function of current injection
'''
with open(self.datapath + 'RatevsI' + self.version, 'rb') as f:
result = pickle.load(f)
x0 = [174.85951028, 45.16030095, 2.89003711]
bnds = ((100, 500), (10, 1000), (0, 5))
res = scipy.optimize.minimize(
lambda p: self.obj_func_RatevsI(p, result),
x0,
bounds=bnds,
method='SLSQP',
options={
'maxiter': 100,
'disp': True
})
print res.x
p = res.x
xdata = result['clamped_current_list']
ydata = result['fr_list']
ymodel, _ = self.obj_func_RatevsI(p, result, rich_return=True)
fig = MyFigure(figsize=(2, 1.5))
pl = fig.addplot(rect=[0.25, 0.25, 0.7, 0.7])
pl.plot(xdata, ydata, color=np.array([55, 126, 184]) / 255)
pl.plot(xdata, ymodel, color='black')
pl.ax.set_yticks([0, 50, 100])
pl.xlabel(r'$I$ (pA)')
pl.ylabel('Firing rate (Hz)')
leg = pl.legend(['Simulation', 'Fit'], loc=2)
leg.draw_frame(False)
fig.save(self.figpath + 'RatevsI' + self.version)
def plot_sNMDAmean(self):
p = MCM.read_params(self.paramsfile)
def meansNMDA(rate, p):
x_mean = rate * p['tauNMDARise']
temp = p['alphaNMDA'] * p['tauNMDADecay'] * x_mean
s_mean = temp / (1 + temp)
return s_mean
theta = 0.645 # make threshold around 30 Hz
#theta = 0.6784
gGABA = 0.6
# NMDA channel voltage-glutamate input relationship
fig = MyFigure(figsize=(1.9, 1.5))
pl = fig.addplot([0.4, 0.3, 0.5, 0.65])
rates = linspace(0, 300, 100)
meanNMDAplot = meansNMDA(rates, p)
meanNMDAplot2 = meanNMDAplot / (1 + gGABA)
pl.plot([0, 300], [theta] * 2,
'--',
color=array([189, 189, 189]) / 255)
pl.plot(rates, meanNMDAplot, color=colorMatrix[1], label='0')
pl.plot(rates,
meanNMDAplot2,
color=colorMatrix[5],
label='%0.1f' % gGABA)
xlabel('NMDA input rate (Hz)')
ylabel('$g_{\mathrm{NMDA}}/(g_L+g_{\mathrm{GABA}})$')
pl.ax.set_yticks([0, theta])
pl.ax.set_yticklabels(['0', r'$\theta_{\mathrm{NMDA}}$'])
pl.ax.set_xticks([0, 150, 300])
leg = legend(title='$g_{\mathrm{GABA}}/g_L$',
loc=4,
bbox_to_anchor=[1.2, -0.05])
leg.draw_frame(False)
fig.save(self.figpath + 'sNMDAmean' + self.version)
def fit_RatevsDendV(self):
'''
fit the somatic firing rate as a function of mean dendritic voltage
'''
with open(self.datapath+'RatevsDendV'+self.version,'rb') as f:
result = pickle.load(f)
x0 = [1.5,0.08,2.2]
bnds = ((0,5),(0,1),(1,3))
params = copy.copy(self.params)
for key in result['params']:
params[key] = result['params'][key] # overwrite the old value
res = scipy.optimize.minimize(lambda p: self.obj_func_RatevsDendV(p,result),
x0,bounds=bnds,method='SLSQP',
options={'maxiter':100, 'disp':True})
print res.x
p = res.x
xdata = result['clamped_dendvolt_list'] + 70
ydata = result['fr_list']
ymodel, _ = self.obj_func_RatevsDendV(p,result,rich_return=True)
fig=MyFigure(figsize=(2,1.5))
pl = fig.addplot(rect=[0.25,0.25,0.7,0.7])
pl.plot(xdata-70,ydata,color=np.array([55,126,184])/255)
pl.plot(xdata-70,ymodel,color='black')
pl.ax.set_xticks([-70,-60,-50,-40])
pl.ax.set_yticks([0,50,100,150])
pl.xlabel(r'$\langle \overline{V}_D\rangle$ (mV)')
pl.ylabel('Firing rate (Hz)')
leg = pl.legend(['Simulation','Fit'],loc=2)
leg.draw_frame(False)
fig.save(self.figpath+'RatevsDendV'+self.version)
def quick_runNewsome_psychometric(self,p,savenew=True,savename=''):
datacolor3 = np.array([150,150,150])/255
modelcolor3 = np.array([37,37,37])/255
m_options = np.array([-0.5,-0.15,-0.05,0.05,0.15,0.5])
c_options = np.array([-0.5,-0.18,-0.06,0.06,0.18,0.5])
n_options = 6
mks = 5
n_plot = 51
coh_plot = np.linspace(-0.6,0.6,n_plot)
performance_plot_m = np.zeros((n_options,n_plot))
for i_c in xrange(n_options):
for i_m in xrange(n_plot):
c = c_options[i_c]
m = coh_plot[i_m]
performance = self.quick_Newsome_model(p,m,c)
performance_plot_m[i_c,i_m] += performance*100
fig=MyFigure(figsize=(4.5,1.7))
pl = fig.addplot(rect=[0.1,0.3,0.24,0.6])
pl.plot(m_options*100,self.p_m_m*100,'o',color=datacolor3,markeredgecolor=datacolor3,label='Data',markersize=mks)
pl.plot(coh_plot*100,performance_plot_m.mean(axis=0),color = modelcolor3,label='Model')
pylab.ylim([-5,105])
pylab.yticks([0,25,50,75,100])
pylab.xticks([-50,-15,15,50],['-50\n Left','-15','15','50\n Right'])
pylab.ylabel('Choices to right (%)')
pylab.xlabel('Motion coherence')
leg = pylab.legend(loc=2,numpoints=1, bbox_to_anchor=(-0.03, 1.15))
leg.draw_frame(False)
m_options = c_options[:]
performance_plot_c = np.zeros((n_options,n_plot))
for i_m in xrange(n_options):
for i_c in xrange(n_plot):
c = coh_plot[i_c]
m = m_options[i_m]
performance = self.quick_Newsome_model(p,m,c)
performance_plot_c[i_m,i_c] += performance*100
pl = fig.addplot(rect=[0.44,0.3,0.24,0.6])
pl.plot(c_options*100,self.p_m_c*100,'o',color=datacolor3,markeredgecolor=datacolor3,label='Data',markersize=mks)
pl.plot(coh_plot*100,performance_plot_c.mean(axis=0),color = modelcolor3,label='Model')
pylab.ylim([-5,105])
pylab.yticks([0,25,50,75,100],[])
pylab.xticks([-50,-15,15,50],['-50\n Red','-15','15','50\n Green'])
pylab.xlabel('Color coherence')
pylab.ylabel('Choices to green (%)')
#title('Motion Context')
leg = pylab.legend(loc=2,numpoints=1, bbox_to_anchor=(-0.03, 1.15))
leg.draw_frame(False)
pl = fig.addplot(rect=[0.73,0.3,0.24,0.6])
for i_m in xrange(n_options//2):
pl.plot(coh_plot*100,(performance_plot_c[i_m,:]+performance_plot_c[-1-i_m,:])/2,color=motion_colors4[-i_m-1])
pylab.ylim([-5,105])
pylab.yticks([0,25,50,75,100],[])
pylab.xticks([-50,-15,15,50],['-50\n Red','-15','15','50\n Green'])
leg = pylab.legend(['Strong','Medium','Weak'],title='Motion coherence',loc=2,bbox_to_anchor=(-0.1, 1.2))
leg.draw_frame(False)
pylab.xlabel('Color coherence')
if savenew:
fig.save(self.figpath+'CompareWithNewsomePsychometric_'+savename+self.monkey+self.version)
combined_panel = True
if combined_panel:
mks = 3
fig=MyFigure(figsize=(1.85,1.5))
pl = fig.addplot(rect=[0.3,0.25,0.6,0.55])
pl.plot(c_options*100,self.p_m_c*100,'o',color=np.array([8,29,88])/255,
markeredgecolor=np.array([8,29,88])/255,markersize=mks,label='Data - Color context')
pl.plot(coh_plot*100,performance_plot_c.mean(axis=0),color = np.array([8,29,88])/255)
pl.plot(m_options*100,self.p_m_m*100,'o',color=np.array([65,182,196])/255,
markeredgecolor=np.array([65,182,196])/255,label='Data - Motion context',markersize=mks)
pl.plot(coh_plot*100,performance_plot_m.mean(axis=0),color = np.array([65,182,196])/255,label='Model')
pylab.ylim([-5,105])
pylab.yticks([0,25,50,75,100])
pylab.xticks([-50,-15,15,50],['-50','-15','15','50'])
pylab.ylabel('Choices to right (%)')
pylab.xlabel('Motion coherence to right')
leg = pylab.legend(loc=2,numpoints=1, bbox_to_anchor=(-0.03, 1.35))
leg.draw_frame(False)
fig.save(self.figpath+'CompareWithNewsomePsychometric_combpanel_'+savename+self.monkey+self.version)
def plot_EvsDI(self,name = 'NMDA',denseinh = False,bothinh = True,rate_range=60):
'''
plot mean dendritic voltage as a function of excitatory input rate,
and inhibitory rate
'''
if denseinh:
di = 'denseinh_'
k = 10
else:
di = ''
k = 1
name_append = di+name+('_range%d' % rate_range)+self.version
with open(self.datapath+'EvsDI_'+di+name+('_range%d' % rate_range)+'_latest.pkl','rb') as f:
res = pickle.load(f)
if rate_range>100:
figsize = (1.5,1.3)
ylim = (-70,0)
yticks = [-70,-10]
rect = [0.25,0.3,0.65,0.65]
else:
figsize = (1.3,1.3)
ylim = (-70,-10)
yticks = [-70,-20]
rect = [0.3,0.3,0.65,0.65]
fig=MyFigure(figsize=figsize)
pl = fig.addplot(rect=rect)
pl.plot(res['pre_rates'],res['vDend_mean'][0],color=colorMatrix[1],label='0')
print res['vDend_mean'][0]
if bothinh:
pl.plot(res['pre_rates'],res['vDend_mean'][30],color=colorMatrix[5],label='30')
#leg=legend(title='Inhibition (Hz)',loc=2, bbox_to_anchor=[-0.05, 1.05],frameon=False)
pl.ax.set_ylim(ylim)
pl.ax.set_yticks(yticks)
xticks = [0,int(rate_range/2),rate_range]
pl.ax.set_xticks(xticks)
#xlabel('Rate of %d ' % p['num_inputeach']+name+' inputs (Hz)' )
xlabel('Rate (Hz)')
if name is 'NMDA':
ylabel('$\overline{V}_D$ (mV)',labelpad=-5)
else:
pl.ax.set_yticklabels(['',''])
if bothinh:
fig.save(self.figpath+'EvsDI_'+name_append)
else:
fig.save(self.figpath+'EvsDI_preinh_'+name_append)
if name is 'NMDA':
inh_list = res['vDend_mean'].keys()
inh_list = sort(inh_list)
fig=MyFigure(figsize=(2.2,1.6))
pl = fig.addplot(rect=[0.25,0.25,0.7,0.7])
for i in xrange(len(inh_list)):
color = colorMatrix[i+1,:]
pl.plot(res['pre_rates'],res['vDend_mean'][inh_list[i]],
color=color,label='%d' % (inh_list[i]*k))
leg=legend(title='Inhibition (Hz)',loc=2, bbox_to_anchor=[0.02, 1.05],frameon=False)
pl.ax.set_ylim((-70,-10))
pl.ax.set_yticks([-70,-20])
pl.ax.set_xticks(xticks)
#xlabel('Rate of %d ' % p['num_inputeach']+name+' inputs (Hz)' )
xlabel('Excitatory input rate (Hz)')
ylabel('Mean dendritic voltage (mV)',labelpad=5)
fig.save(self.figpath+'EvsDI_full_'+name_append)
def plot_WeightChangevsInitWeight(self):
post_rate = 10
with open(self.datapath + 'learning_para_2014-11-10_0', 'rb') as f:
para = pickle.load(f)
q = para.copy()
with open(
self.datapath + 'WeightChangevsInitWeight_postr%d' % post_rate,
'rb') as f:
resultlist = pickle.load(f)
# in vivo change (see Higgins, Graupner, Brunel 2014)
q = self.invivo_learningparams(q)
vDend_mean_list = list()
fig = MyFigure(figsize=(4, 3))
pl = fig.addplot(rect=[0.2, 0.15, 0.7, 0.8])
pl.plot([q['w0'], q['w1']], [q['w0'], q['w1']],
color='black',
linestyle='--')
i = 0
for result in resultlist:
p = result['params']
vDend = result['vDend']
NMDACasyn = result['NMDACasyn']
bAPCa = result['bAPCa']
vDend_mean = vDend.mean(axis=1)
vDend_mean = vDend_mean.reshape(p['n_weight'], p['n_rep'])
vDend_mean = vDend_mean.mean(axis=1)
vDend_mean_list.append(vDend_mean)
num_syn = NMDACasyn.shape[0]
num_soma = bAPCa.shape[0]
synsomaratio = num_syn // num_soma
bAPCasyn = bAPCa.repeat(synsomaratio, axis=0)
CaTrace = NMDACasyn * q['NMDA_scaling'] + bAPCasyn * q[
'bAP_scaling']
w_post = np.zeros(num_syn)
n_eachweight = num_syn // p['n_weight']
total_time = 3000 #s
record_dt = p['record_dt']
for i_syn in xrange(num_syn):
CaTraceSorted = np.sort(CaTrace[i_syn])
AlphaP = MCM.crossthresholdtime(
CaTraceSorted,
q['thetaP']) * record_dt * (total_time / p['runtime'])
AlphaD = MCM.crossthresholdtime(
CaTraceSorted,
q['thetaD']) * record_dt * (total_time / p['runtime'])
wpre = p['weights'][i_syn // n_eachweight]
wpost_syn = MCM.SynapseChange(wpre, AlphaP, AlphaD, q)
w_post[i_syn] = wpost_syn
w_post = w_post.reshape(p['n_weight'], n_eachweight)
w_post = w_post.mean(axis=1)
ax, = pl.plot(p['weights'],
w_post,
color=colorMatrix[i * 0, :],
label='%d' % result['dend_inh_rate'])
i += 1
plt.xlabel('Weight before learning')
plt.ylabel('Weight after learning')
pl.ax.set_plt.xlim((0, 3))
pl.ax.set_xticks([0, 1, 2, 3])
pl.ax.set_plt.ylim((0, 3))
pl.ax.set_yticks([0, 1, 2, 3])
leg = plt.legend(title='Inhibition (Hz)', loc=2)
leg.draw_frame(False)
axis('equal')
#title('%d NMDA inputs of %d Hz' % (num_input,pre_rate))
fig.save(self.figpath + ('WeightChangevsInitWeight_%d' % post_rate) +
self.version)
def plot_vary_inh(self,savenew=True,plot_ylabel=True):
p = MCM.read_params(self.paramsfile)
p['inh_rate_base'] = 5
p['num_soma'] = 1
p['num_DendEach'] = 10
p['num_path'] = 2
p['num_DendDisinh'] = 3
p['mode'] = 'control_voltage'
p['v_target'] = -40
single_gating_list = list()
single_gating_nonoverlap_list = list()
num_dend_list = list()
leg_list = list()
inh_list = [30,20,10]
for inh in inh_list:
p['inh_rate_max'] = inh + p['inh_rate_base']
p['inh_base'] = p['inh_rate_base']*p['tauGABA']*p['gGABA']/nsiemens # rate * tau_GABA * gGABA
p['inh_max'] = p['inh_rate_max']*p['tauGABA']*p['gGABA']/nsiemens # rate * tau_GABA * gGABA
num_dend_plot = range(p['num_DendDisinh'],50)
single_gating_plot = list()
for num_dend in num_dend_plot:
p['num_DendEach'] = num_dend
res = self.analytic_evalgating(p)
single_gating_plot.append(res['single_gating'])
num_dend_list.append(num_dend_plot)
single_gating_list.append(single_gating_plot)
leg_list.append(str(inh))
res = self.analytic_evalgating(p,no_overlap=True)
single_gating_nonoverlap_list.append(res['single_gating'])
# Single path
fig=MyFigure(figsize=(1.5,1.5))
pl = fig.addplot(rect=[0.3,0.3,0.65,0.65])
for i in xrange(len(inh_list)):
pl.plot(num_dend_list[i][0]/array(num_dend_list[i]),single_gating_list[i],
color=colorMatrix1[2*i+1])
ylim((0,1))
xlim((0,1))
xticks([0,0.5,1],['0','0.5','1'])
xlabel('$N_{disinh}/N_{dend}$')
if plot_ylabel:
ylabel('Gating selectivity')
yticks([0,0.5,1],['0','0.5','1'])
else:
yticks([0,0.5,1],['','',''])
leg = legend(leg_list,title='Disinhibition (Hz)',loc=1, bbox_to_anchor=[1.1, 1.1])
leg.draw_frame(False)
for i in xrange(len(inh_list)):
pl.plot([0],single_gating_nonoverlap_list[i],'D',markerfacecolor=colorMatrix1[2*i+1],
markeredgecolor = 'none',markersize=4)
if savenew:
fig.save(self.figpath+'GatingCapacitySinglePathVaryInh'+self.version)
def plot_WeightChangevsrE(self):
'''
Plot weight change when varying excitatory input rate
'''
with open(self.datapath + 'resultlist_varyrate_postr10', 'rb') as f:
result_list = pickle.load(f)
with open(self.datapath + 'learning_para_2014-11-10_0', 'rb') as f:
para = pickle.load(f)
q = para.copy()
# in vivo change (see Higgins, Graupner, Brunel 2014)
#q['NMDA_scaling'] = q['NMDA_scaling']*1.5/2
#q['bAP_scaling'] = q['bAP_scaling']/5
vDend_mean_list = list()
w_post_list = list()
AlphaP_list = list()
AlphaD_list = list()
for result in result_list:
p = result['params']
vDend = result['vDend']
NMDACasyn = result['NMDACasyn']
bAPCa = result['bAPCa']
vDend_mean = vDend.mean(axis=1)
vDend_mean_list.append(vDend_mean)
num_syn = NMDACasyn.shape[0]
num_soma = bAPCa.shape[0]
synsomaratio = num_syn // num_soma
bAPCasyn = bAPCa.repeat(synsomaratio, axis=0)
CaTrace = NMDACasyn * q['NMDA_scaling'] + bAPCasyn * q[
'bAP_scaling']
w_post = zeros(num_syn)
AlphaP_plot = zeros(num_syn)
AlphaD_plot = zeros(num_syn)
n_eachrate = num_syn // p['n_rate']
repeat_times = 5
record_dt = p['record_dt']
for i_syn in xrange(num_syn):
CaTraceSorted = sort(CaTrace[i_syn])
AlphaP = MCM.crossthresholdtime(
CaTraceSorted, q['thetaP']) * record_dt * repeat_times
AlphaD = MCM.crossthresholdtime(
CaTraceSorted, q['thetaD']) * record_dt * repeat_times
wpre = 1
wpost_syn = MCM.SynapseChange(wpre, AlphaP, AlphaD, q)
w_post[i_syn] = wpost_syn
AlphaP_plot[i_syn] = AlphaP
AlphaD_plot[i_syn] = AlphaD
w_post = w_post.reshape(p['n_rate'], n_eachrate)
w_post = w_post.mean(axis=1)
w_post_list.append(w_post)
AlphaP_list.append(AlphaP_plot)
AlphaD_list.append(AlphaD_plot)
dend_inh_rates = p['dend_inh_rates']
colorMatrix = array([[65, 182, 196], [34, 94, 168], [8, 29, 88]]) / 255
fig = MyFigure(figsize=(2.5, 2.5))
pl = fig.addplot(rect=[0.2, 0.25, 0.7, 0.7])
plotlist = list()
i = 0
for vDend_mean in vDend_mean_list:
ax, = pl.plot(p['pre_rates'],
vDend_mean / mV,
color=colorMatrix[i, :])
plotlist.append(ax)
xlabel('Pre-synaptic rate (Hz)')
ylabel('Mean Dendritic Voltage (mV)')
i += 1
pl.ax.set_xticks([0, 20, 40])
pl.ax.set_ylim((-70, -10))
pl.ax.set_yticks([-70, -50, -30, -10])
leg = legend(plotlist, ['%d' % r for r in dend_inh_rates],
title='Inhibition (Hz)',
loc=2)
leg.draw_frame(False)
#fig.save('figures/RateVsInhibition'+self.version)
fig = MyFigure(figsize=(2, 2))
pl = fig.addplot(rect=[0.2, 0.2, 0.7, 0.7])
pl.plot([0, max(p['pre_rates'])], [1, 1],
color=array([189, 189, 189]) / 255)
i = 0
plotlist = list()
for w_post in w_post_list:
ax, = pl.plot(p['pre_rates'], w_post, color=colorMatrix[i, :])
plotlist.append(ax)
xlabel('Pre-synaptic rate (Hz)')
ylabel('Weight change')
i += 1
pl.ax.set_xticks([0, 20, 40])
pl.ax.set_ylim((0, 3))
pl.ax.set_yticks([0, 1, 2, 3])
leg = legend(plotlist, ['%d' % r for r in dend_inh_rates],
title='Inhibition (Hz)',
loc=2)
leg.draw_frame(False)
#title('post rate = %d Hz' % p['post_rate'])
fig.save(self.figpath + 'WeightChangevsrE_postr%d' % p['post_rate'] +
self.version)
def plot_WeightChangevsInitWeight(self):
post_rate = 10
with open(self.datapath+'learning_para_2014-11-10_0','rb') as f:
para = pickle.load(f)
q = para.copy()
with open(self.datapath+'WeightChangevsInitWeight_postr%d' % post_rate,'rb') as f:
resultlist = pickle.load(f)
# in vivo change (see Higgins, Graupner, Brunel 2014)
q = self.invivo_learningparams(q)
vDend_mean_list = list()
fig=MyFigure(figsize=(4,3))
pl = fig.addplot(rect=[0.2,0.15,0.7,0.8])
pl.plot([q['w0'],q['w1']],[q['w0'],q['w1']],color='black',linestyle='--')
i = 0
for result in resultlist:
p = result['params']
vDend = result['vDend']
NMDACasyn = result['NMDACasyn']
bAPCa = result['bAPCa']
vDend_mean = vDend.mean(axis=1)
vDend_mean = vDend_mean.reshape(p['n_weight'],p['n_rep'])
vDend_mean = vDend_mean.mean(axis=1)
vDend_mean_list.append(vDend_mean)
num_syn = NMDACasyn.shape[0]
num_soma = bAPCa.shape[0]
synsomaratio = num_syn//num_soma
bAPCasyn = bAPCa.repeat(synsomaratio,axis=0)
CaTrace = NMDACasyn*q['NMDA_scaling'] + bAPCasyn*q['bAP_scaling']
w_post = np.zeros(num_syn)
n_eachweight = num_syn//p['n_weight']
total_time = 3000 #s
record_dt = p['record_dt']
for i_syn in xrange(num_syn):
CaTraceSorted = np.sort(CaTrace[i_syn])
AlphaP = MCM.crossthresholdtime(CaTraceSorted,q['thetaP'])*record_dt*(total_time/p['runtime'])
AlphaD = MCM.crossthresholdtime(CaTraceSorted,q['thetaD'])*record_dt*(total_time/p['runtime'])
wpre = p['weights'][i_syn//n_eachweight]
wpost_syn = MCM.SynapseChange(wpre,AlphaP,AlphaD,q)
w_post[i_syn] = wpost_syn
w_post = w_post.reshape(p['n_weight'],n_eachweight)
w_post = w_post.mean(axis=1)
ax, = pl.plot(p['weights'],w_post,color=colorMatrix[i*0,:],label='%d' % result['dend_inh_rate'])
i += 1
plt.xlabel('Weight before learning')
plt.ylabel('Weight after learning')
pl.ax.set_plt.xlim((0,3))
pl.ax.set_xticks([0,1,2,3])
pl.ax.set_plt.ylim((0,3))
pl.ax.set_yticks([0,1,2,3])
leg=plt.legend(title='Inhibition (Hz)',loc=2)
leg.draw_frame(False)
axis('equal')
#title('%d NMDA inputs of %d Hz' % (num_input,pre_rate))
fig.save(self.figpath+('WeightChangevsInitWeight_%d' % post_rate)+self.version)
def fit_experiment(self):
global Nfeval
experiment3B = 'NevianFig3B'
with open(self.datapath + 'resultlist_' + experiment3B, 'rb') as f:
result_list3B = pickle.load(f)
experiment3D = 'NevianFig3D'
with open(self.datapath + 'resultlist_' + experiment3D, 'rb') as f:
result_list3D = pickle.load(f)
experiment3D_full = 'NevianFig3D_full'
with open(self.datapath + 'resultlist_' + experiment3D_full,
'rb') as f:
result_list3D_full = pickle.load(f)
experiment2 = 'NevianFig2'
with open(self.datapath + 'resultlist_' + experiment2, 'rb') as f:
result_list2 = pickle.load(f)
experiment2_full = 'NevianFig2_full'
with open(self.datapath + 'resultlist_' + experiment2_full, 'rb') as f:
result_list2_full = pickle.load(f)
x0 = list()
bnds = list()
# The following is the most up-to-date parameters fitting Nevian Fig.2
x0.append(0.371) # NMDA scaling parameter
bnds.append([0.3, 3])
x0.append(0.957) # bAP scaling
bnds.append([0.4, 1])
x0.append(39.949) # induced depression strength
bnds.append([20, 500])
x0.append(177.552) # induced potentiation strength
bnds.append([50, 500])
#x0.append(346.833) # synaptic learning time constant (s)
#bnds.append([50,700])
x0.append(
2.78) # potentiation threshold (this parameter is very important)
bnds.append([1.2, 3])
#x0.append(20)
#bnds.append([12,100]) # Ca time constant (ms)
#x0.append(0.647)
#bnds.append([0.35,70]) # noise constant sigma
start = time.clock()
# Fit the model to some of the data but not all
obj_func = lambda para: \
0*self.get_model_sqe(para, result_list3B, experiment3B) + \
1*self.get_model_sqe(para, result_list3D, experiment3D) + \
1*self.get_model_sqe(para, result_list2, experiment2)
Nfeval = 1
def callbackF(Xi):
global Nfeval
if Nfeval == 1:
print 'N f NMDA bAP wpre w1 gP tau tau_Ca'
print('%2d %0.3f ' % (Nfeval, obj_func(Xi)) +
' '.join('{:0.3f}'.format(k) for k in Xi))
Nfeval += 1
res = scipy.optimize.minimize(obj_func,
x0,
bounds=bnds,
method='SLSQP',
options={
'maxiter': 300,
'disp': True
},
callback=callbackF)
x = res.x
print('%2d %0.3f ' % (Nfeval, obj_func(x)) +
' '.join('{:0.3f}'.format(k) for k in x))
end = time.clock()
print 'time spent %0.6f' % (end - start)
sqe, change_list, wpost_list, para = self.get_model_sqe(
x, result_list2, rich_return=True)
fig = MyFigure(figsize=(2, 2))
pl = fig.addplot(rect=[0.2, 0.15, 0.7, 0.8])
self.plot_model_result(pl,
para,
result_list2_full,
experiment2_full,
plot_data=True)
self.compareWithNevian_CaTrace(para,
remakeFig=False,
experiment='NevianFig5')
fig = MyFigure(figsize=(1.5, 2))
pl = fig.addplot(rect=[0.2, 0.15, 0.7, 0.8])
self.plot_model_result(pl,
para,
result_list3B,
experiment3B,
plot_data=True)
#compareWithNevian_CaTrace(para,remakeFig = False,experiment = experiment3B)
fig = MyFigure(figsize=(1.5, 2))
pl = fig.addplot(rect=[0.2, 0.15, 0.7, 0.8])
self.plot_model_result(pl,
para,
result_list3D_full,
experiment3D_full,
plot_data=True)
def fit_experiment(self):
global Nfeval
experiment3B = 'NevianFig3B'
with open(self.datapath+'resultlist_'+experiment3B,'rb') as f:
result_list3B = pickle.load(f)
experiment3D = 'NevianFig3D'
with open(self.datapath+'resultlist_'+experiment3D,'rb') as f:
result_list3D = pickle.load(f)
experiment3D_full = 'NevianFig3D_full'
with open(self.datapath+'resultlist_'+experiment3D_full,'rb') as f:
result_list3D_full = pickle.load(f)
experiment2 = 'NevianFig2'
with open(self.datapath+'resultlist_'+experiment2,'rb') as f:
result_list2 = pickle.load(f)
experiment2_full = 'NevianFig2_full'
with open(self.datapath+'resultlist_'+experiment2_full,'rb') as f:
result_list2_full = pickle.load(f)
x0 = list()
bnds = list()
# The following is the most up-to-date parameters fitting Nevian Fig.2
x0.append(0.371) # NMDA scaling parameter
bnds.append([0.3,3])
x0.append(0.957) # bAP scaling
bnds.append([0.4,1])
x0.append(39.949) # induced depression strength
bnds.append([20,500])
x0.append(177.552) # induced potentiation strength
bnds.append([50,500])
#x0.append(346.833) # synaptic learning time constant (s)
#bnds.append([50,700])
x0.append(2.78) # potentiation threshold (this parameter is very important)
bnds.append([1.2,3])
#x0.append(20)
#bnds.append([12,100]) # Ca time constant (ms)
#x0.append(0.647)
#bnds.append([0.35,70]) # noise constant sigma
start = time.clock()
# Fit the model to some of the data but not all
obj_func = lambda para: \
0*self.get_model_sqe(para, result_list3B, experiment3B) + \
1*self.get_model_sqe(para, result_list3D, experiment3D) + \
1*self.get_model_sqe(para, result_list2, experiment2)
Nfeval = 1
def callbackF(Xi):
global Nfeval
if Nfeval == 1:
print 'N f NMDA bAP wpre w1 gP tau tau_Ca'
print('%2d %0.3f ' % (Nfeval,obj_func(Xi))+' '.join('{:0.3f}'.format(k) for k in Xi))
Nfeval += 1
res = scipy.optimize.minimize(obj_func,x0,bounds=bnds,method='SLSQP',
options={'maxiter':300, 'disp':True},callback=callbackF)
x = res.x
print('%2d %0.3f ' % (Nfeval,obj_func(x))+' '.join('{:0.3f}'.format(k) for k in x))
end = time.clock()
print 'time spent %0.6f' % (end-start)
sqe, change_list, wpost_list, para = self.get_model_sqe(x, result_list2, rich_return=True)
fig=MyFigure(figsize=(2,2))
pl = fig.addplot(rect=[0.2,0.15,0.7,0.8])
self.plot_model_result(pl, para, result_list2_full, experiment2_full, plot_data=True)
self.compareWithNevian_CaTrace(para,remakeFig = False,experiment = 'NevianFig5')
fig=MyFigure(figsize=(1.5,2))
pl = fig.addplot(rect=[0.2,0.15,0.7,0.8])
self.plot_model_result(pl, para, result_list3B, experiment3B, plot_data=True)
#compareWithNevian_CaTrace(para,remakeFig = False,experiment = experiment3B)
fig=MyFigure(figsize=(1.5,2))
pl = fig.addplot(rect=[0.2,0.15,0.7,0.8])
self.plot_model_result(pl, para, result_list3D_full, experiment3D_full, plot_data=True)
def quick_runNewsome_psychometric(self, p, savenew=True, savename=''):
datacolor3 = np.array([150, 150, 150]) / 255
modelcolor3 = np.array([37, 37, 37]) / 255
m_options = np.array([-0.5, -0.15, -0.05, 0.05, 0.15, 0.5])
c_options = np.array([-0.5, -0.18, -0.06, 0.06, 0.18, 0.5])
n_options = 6
mks = 5
n_plot = 51
coh_plot = np.linspace(-0.6, 0.6, n_plot)
performance_plot_m = np.zeros((n_options, n_plot))
for i_c in xrange(n_options):
for i_m in xrange(n_plot):
c = c_options[i_c]
m = coh_plot[i_m]
performance = self.quick_Newsome_model(p, m, c)
performance_plot_m[i_c, i_m] += performance * 100
fig = MyFigure(figsize=(4.5, 1.7))
pl = fig.addplot(rect=[0.1, 0.3, 0.24, 0.6])
pl.plot(m_options * 100,
self.p_m_m * 100,
'o',
color=datacolor3,
markeredgecolor=datacolor3,
label='Data',
markersize=mks)
pl.plot(coh_plot * 100,
performance_plot_m.mean(axis=0),
color=modelcolor3,
label='Model')
pylab.ylim([-5, 105])
pylab.yticks([0, 25, 50, 75, 100])
pylab.xticks([-50, -15, 15, 50],
['-50\n Left', '-15', '15', '50\n Right'])
pylab.ylabel('Choices to right (%)')
pylab.xlabel('Motion coherence')
leg = pylab.legend(loc=2, numpoints=1, bbox_to_anchor=(-0.03, 1.15))
leg.draw_frame(False)
m_options = c_options[:]
performance_plot_c = np.zeros((n_options, n_plot))
for i_m in xrange(n_options):
for i_c in xrange(n_plot):
c = coh_plot[i_c]
m = m_options[i_m]
performance = self.quick_Newsome_model(p, m, c)
performance_plot_c[i_m, i_c] += performance * 100
pl = fig.addplot(rect=[0.44, 0.3, 0.24, 0.6])
pl.plot(c_options * 100,
self.p_m_c * 100,
'o',
color=datacolor3,
markeredgecolor=datacolor3,
label='Data',
markersize=mks)
pl.plot(coh_plot * 100,
performance_plot_c.mean(axis=0),
color=modelcolor3,
label='Model')
pylab.ylim([-5, 105])
pylab.yticks([0, 25, 50, 75, 100], [])
pylab.xticks([-50, -15, 15, 50],
['-50\n Red', '-15', '15', '50\n Green'])
pylab.xlabel('Color coherence')
pylab.ylabel('Choices to green (%)')
#title('Motion Context')
leg = pylab.legend(loc=2, numpoints=1, bbox_to_anchor=(-0.03, 1.15))
leg.draw_frame(False)
pl = fig.addplot(rect=[0.73, 0.3, 0.24, 0.6])
for i_m in xrange(n_options // 2):
pl.plot(coh_plot * 100,
(performance_plot_c[i_m, :] +
performance_plot_c[-1 - i_m, :]) / 2,
color=motion_colors4[-i_m - 1])
pylab.ylim([-5, 105])
pylab.yticks([0, 25, 50, 75, 100], [])
pylab.xticks([-50, -15, 15, 50],
['-50\n Red', '-15', '15', '50\n Green'])
leg = pylab.legend(['Strong', 'Medium', 'Weak'],
title='Motion coherence',
loc=2,
bbox_to_anchor=(-0.1, 1.2))
leg.draw_frame(False)
pylab.xlabel('Color coherence')
if savenew:
fig.save(self.figpath + 'CompareWithNewsomePsychometric_' +
savename + self.monkey + self.version)
combined_panel = True
if combined_panel:
mks = 3
fig = MyFigure(figsize=(1.85, 1.5))
pl = fig.addplot(rect=[0.3, 0.25, 0.6, 0.55])
pl.plot(c_options * 100,
self.p_m_c * 100,
'o',
color=np.array([8, 29, 88]) / 255,
markeredgecolor=np.array([8, 29, 88]) / 255,
markersize=mks,
label='Data - Color context')
pl.plot(coh_plot * 100,
performance_plot_c.mean(axis=0),
color=np.array([8, 29, 88]) / 255)
pl.plot(m_options * 100,
self.p_m_m * 100,
'o',
color=np.array([65, 182, 196]) / 255,
markeredgecolor=np.array([65, 182, 196]) / 255,
label='Data - Motion context',
markersize=mks)
pl.plot(coh_plot * 100,
performance_plot_m.mean(axis=0),
color=np.array([65, 182, 196]) / 255,
label='Model')
pylab.ylim([-5, 105])
pylab.yticks([0, 25, 50, 75, 100])
pylab.xticks([-50, -15, 15, 50], ['-50', '-15', '15', '50'])
pylab.ylabel('Choices to right (%)')
pylab.xlabel('Motion coherence to right')
leg = pylab.legend(loc=2,
numpoints=1,
bbox_to_anchor=(-0.03, 1.35))
leg.draw_frame(False)
fig.save(self.figpath +
'CompareWithNewsomePsychometric_combpanel_' + savename +
self.monkey + self.version)
def plot_voltagedistribution(self, denseinh=False):
'''
Plot voltage distribution
'''
p = dict()
p['num_DendEach'] = 1
p['dt'] = 0.2 * ms
p['num_Soma'] = 40
p['pre_rate'] = 40 * Hz
p['inh_base'] = 5 * Hz
if denseinh:
p['gGABA'] = p['gGABA'] / 10
p['inh_base'] = p['inh_base'] * 10
self.version = 'denseinh_' + self.version
pre_rates = ones(p['num_Soma']) * p['pre_rate']
model = MCM.Model(paramsfile=self.paramsfile,
eqsfile=self.eqsfile,
outsidePara=p)
model.make_model_dendrite_only(num_soma=p['num_Soma'],
clamped_somavolt=-60 * mV,
condition='invivo')
model.make_dend_bkginh(inh_rates=p['inh_base'])
model.single_pathway_gating_experiment(pre_rates,
num_input=15,
w_input=1)
model.make_network()
model.reinit()
net = Network(model)
net.run(2.5 * second, report='text')
mon = model.monitor
times = mon['MvDend'].times / ms
plotstart = 500
dendv = mon['MvDend'].values[:, times > plotstart] / mV
dendv = dendv[range(0, p['num_DendEach'] *
p['num_Soma'], p['num_DendEach']), :]
res = dict()
res['params'] = p
res['dendv'] = dendv
with open(self.datapath + 'voltage_distribution_' + self.version,
'wb') as f:
pickle.dump(res, f)
fig = MyFigure(figsize=(1.5, 1.5))
pl = fig.addplot(rect=[0.05, 0.3, 0.9, 0.65])
#pl.ax.set_frame_on(False)
pl.ax.spines['left'].set_visible(False)
pl.ax.get_yaxis().set_visible(False)
n, bins, patches = hist(dendv.flatten(),
50,
normed=1,
histtype='stepfilled')
color_hist = array([99, 99, 99]) / 255
setp(patches, 'facecolor', color_hist, 'edgecolor', color_hist)
xlim([-70, -10])
xticks([-60, -50, -40, -30, -20], ['-60', '', '-40', '', '-20'])
xlabel('$V_D$ (mV)')
fig.save(self.figpath + 'VoltageDistribution_' + self.version)
def plot_compare(self, p, savenew=True, savename=''):
cohs = [0.05, 0.15, 0.5]
x_plot = [-coh for coh in cohs[::-1]] + cohs
x_plot = np.array(x_plot)
p_frac1 = p.copy()
p_frac1['frac_proj'] = 1
r_mc_list = np.zeros((6, 6))
for i_m in xrange(len(x_plot)):
for i_c in xrange(len(x_plot)):
m = x_plot[i_m]
c = x_plot[i_c]
#for a particular value of motion and color m,c \in (-1,1)
#the input from the two pathways to the four considered populations are
#(m,c)
#(-m,c)
#(m,-c)
#(-m,-c)
r_mc_list[i_m,
i_c] = self.analytic_twopath(p_frac1, 40 * (1 + m),
40 * (1 + c))
input_plot_all = r_mc_list - r_mc_list[::-1, ::-1]
win1_list = self.perform_func(input_plot_all)
n_c = n_m = 6
Dim_motion = r_mc_list + r_mc_list[:, ::
-1] - r_mc_list[::
-1, :] - r_mc_list[::
-1, ::
-1]
Dim_color = r_mc_list - r_mc_list[:, ::
-1] + r_mc_list[::
-1, :] - r_mc_list[::
-1, ::
-1]
smalllinewidth = 1
largelinewidth = 2.5
Dim_motion = Dim_motion.mean(axis=1)
fig = MyFigure(figsize=(4, 4))
pl = fig.addplot(rect=[0.2, 0.2, 0.7, 0.7])
for i_motion in xrange(n_m):
if i_motion < (n_m / 2):
facecolor = 'white'
linewidth = smalllinewidth
linecolor = motion_colors[i_motion]
px = 1
else:
linecolor = motion_colors[n_m - i_motion - 1]
facecolor = motion_colors[n_m - i_motion - 1]
linewidth = largelinewidth
px = -1
pl.plot(px,
Dim_motion[i_motion],
'o',
markeredgecolor=linecolor,
markerfacecolor=facecolor,
linewidth=linewidth)
Dim_color1 = Dim_color[:3, :].mean(axis=0)
Dim_color2 = Dim_color[3:, :].mean(axis=0)
print Dim_motion
print Dim_color1
for i_color in xrange(n_c):
if i_color < (n_c / 2):
linecolor = color_colors[i_color]
markerfacecolor = 'white'
else:
linecolor = color_colors[n_c - i_color - 1]
markerfacecolor = linecolor
pl.plot(0.5,
Dim_color1[i_color],
'o',
markeredgecolor=linecolor,
markerfacecolor=markerfacecolor,
linewidth=smalllinewidth)
pl.plot(-0.5,
Dim_color2[i_color],
'o',
markeredgecolor=linecolor,
markerfacecolor=markerfacecolor,
linewidth=largelinewidth)
pylab.xlabel('Dim Dec')
pylab.ylabel('Dim Motion/Color')
self.quick_runNewsome_psychometric(p,
savenew=savenew,
savename=savename)
def plot_state_space(self,p,savenew=True):
'''
Plot the state-space plot for fitted model
'''
n_list = 6
record_list = [np.array([])]*n_list
name_list = ['r1','r2','input_mc','input_none','input_m','input_c']
for i_m in xrange(len(self.m_plot)):
for i_c in xrange(len(self.c_plot)):
m = self.m_plot[i_m]
c = self.c_plot[i_c]
result = self.run_Newsome_model(p,m,c, biphasic_input=False)
for i_list in xrange(n_list):
record_list[i_list] = np.concatenate((record_list[i_list],result[name_list[i_list]]))
print 'm = %0.2f, c = %0.2f' % (m,c)
# z-score results
zscore_list = list()
for i_list in xrange(n_list):
zscore = record_list[i_list]
temp = zscore[zscore>0.01] # exclude the data before and after dots
#zscore -= np.mean(record_list[i_list])
zscore = zscore/np.std(temp)
zscore_list.append(zscore)
zs = zscore_list
Dim_dec = -(zs[0]-zs[1])/2
Dim_motion = (zs[2]-zs[3]+zs[4]-zs[5])/4
Dim_color = (zs[2]-zs[3]-zs[4]+zs[5])/4
N_t = len(result['r1'])
maxr = max([max(abs(Dim_dec)),max(abs(Dim_motion)),max(abs(Dim_color))])
smalllinewidth = 0.5
largelinewidth = 1
mks = 2
n_m = 6
n_c = 6
px = [0]*n_m
py = [0]*n_m
for i_cond in xrange(n_m*n_c):
i_motion = i_cond//n_m
px[i_motion] += Dim_dec[i_cond*N_t:(i_cond+1)*N_t]/n_c
py[i_motion] += Dim_motion[i_cond*N_t:(i_cond+1)*N_t]/n_c
dotoffcolor = np.array([84,39,143])/255.
fig=MyFigure(figsize=(3,1.5))
pl = fig.addplot(rect=[0.01,0.05,0.45,0.9])
for i_motion in xrange(n_m):
if i_motion<(n_m/2):
mfc = 'white'
linewidth = smalllinewidth
linecolor = motion_colors[i_motion]
mec = linecolor
else:
linecolor = motion_colors[n_m-i_motion-1]
mfc = motion_colors[n_m-i_motion-1]
linewidth = largelinewidth
mec = mfc
pl.plot([px[i_motion][-1]]*2,[py[i_motion][-1],0],
'o-',color=dotoffcolor,
markerfacecolor=dotoffcolor,markeredgecolor=dotoffcolor,linewidth=linewidth,markersize=mks)
pl.plot(px[i_motion],py[i_motion],
'o-',color=linecolor,
markerfacecolor=mfc,markeredgecolor=mec,linewidth=linewidth,markersize=mks)
#xlabel('Dim Dec')
#ylabel('Dim Motion')
pylab.xlim((-maxr,maxr))
pylab.ylim((-maxr,maxr))
pylab.axis('equal')
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
px_1 = [0]*n_c
py_1 = [0]*n_c
px_2 = [0]*n_c
py_2 = [0]*n_c
for i_cond in xrange(n_m*n_c):
i_color = np.mod(i_cond,n_c)
i_motion = i_cond//n_m
if i_motion<(n_m/2):
px_1[i_color] += Dim_dec[i_cond*N_t:(i_cond+1)*N_t]/n_c*2
py_1[i_color] += Dim_color[i_cond*N_t:(i_cond+1)*N_t]/n_c*2
else:
px_2[i_color] += Dim_dec[i_cond*N_t:(i_cond+1)*N_t]/n_c*2
py_2[i_color] += Dim_color[i_cond*N_t:(i_cond+1)*N_t]/n_c*2
pl = fig.addplot(rect=[0.54,0.05,0.45,0.9])
for i_color in xrange(n_c):
if i_color<(n_c/2):
linecolor = color_colors[i_color]
mfc = 'white'
mec = linecolor
else:
linecolor = color_colors[n_c-i_color-1]
mfc = linecolor
mec = mfc
pl.plot([px_1[i_color][-1]]*2,[py_1[i_color][-1],0],'o-',
color=dotoffcolor,markerfacecolor=dotoffcolor,markeredgecolor=dotoffcolor,linewidth=smalllinewidth,markersize=mks)
pl.plot([px_2[i_color][-1]]*2,[py_2[i_color][-1],0],'o-',
color=dotoffcolor,markerfacecolor=dotoffcolor,markeredgecolor=dotoffcolor,linewidth=largelinewidth,markersize=mks)
pl.plot(px_1[i_color],py_1[i_color],'o-',
color=linecolor,markerfacecolor=mfc,markeredgecolor=mec,linewidth=smalllinewidth,markersize=mks)
pl.plot(px_2[i_color],py_2[i_color],'o-',
color=linecolor,markerfacecolor=mfc,markeredgecolor=mec,linewidth=largelinewidth,markersize=mks)
#xlabel('Dim Dec')
#ylabel('Dim Color')
pylab.xlim((-maxr,maxr))
pylab.ylim((-maxr,maxr))
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
if savenew:
fig.save(self.figpath+'plot_Newsome_statespace'+'_'+self.monkey+self.version)
def fit_DendVvsgErI(self):
'''
fit the dendritic voltage as a function of excitatory input and inhibitory rate
'''
with open(self.datapath + 'DendVvsgErI' + self.version, 'rb') as f:
result = pickle.load(f)
x0 = [5, 10, 7, 1]
bnds = ((1, 20), (1, 20), (1, 20), (0, 3))
params = copy.copy(self.params)
for key in result['params']:
params[key] = result['params'][key] # overwrite the old value
temp = params['pre_rate'] * params['tauNMDARise'] * params[
'tauNMDADecay'] * params['alphaNMDA']
print params['tauNMDARise'] * params['tauNMDADecay'] * params[
'alphaNMDA']
# 0.06 is obtained from tau_rise = 2ms, tau_decay=100ms, alpha=0.3 /ms
s_mean = temp / (1 + temp) # mean gating variable
result['Exc'] = result['weights'] * params['gNMDA'] * 1e9 * params[
'num_input'] * s_mean
# unit-less weights * synaptic conductance 2.5nS * 15 synapses * mean gating variable for 30Hz
result['Inh'] = result['dend_inh_rates'] * params['tauGABA'] * params[
'gGABA'] * 1e9
# Total Rate * synaptic conductance * tau_GABA
res = scipy.optimize.minimize(
lambda p: self.obj_func_DendVvsgErI(p, result),
x0,
bounds=bnds,
method='SLSQP',
options={
'maxiter': 100,
'disp': True
})
print res.x
p = res.x
xdata = result['Exc']
n_curve = len(result['vdend_mean_list'])
fig = MyFigure(figsize=(2, 1.5))
pl = fig.addplot(rect=[0.25, 0.25, 0.7, 0.7])
for i in xrange(n_curve):
ydata = result['vdend_mean_list'][i] * 1000
ax_data, = pl.plot(xdata,
ydata,
color=np.array([55, 126, 184]) / 255)
ymodel_list, sqe = self.obj_func_DendVvsgErI(p,
result,
rich_return=True)
for i in xrange(n_curve):
ax_model, = pl.plot(xdata, ymodel_list[i], color='black')
pl.xlabel(r'$g_{E,\mathrm{tot}}$ (nS)', labelpad=3)
pl.ylabel(r'$\overline{V}_D$ (mV)')
pl.ax.set_xlim((0, 50))
pl.ax.set_xticks([0, 25, 50])
pl.ax.set_ylim((-70, -10))
pl.ax.set_yticks([-70, -20])
leg = pl.legend([ax_data, ax_model], ['Simulation', 'Fit'],
loc=2,
bbox_to_anchor=[0, 1.05])
leg.draw_frame(False)
fig.save(self.figpath + 'DendVvsgErI' + self.version)
def plot_state_space(self, p, savenew=True):
'''
Plot the state-space plot for fitted model
'''
n_list = 6
record_list = [np.array([])] * n_list
name_list = [
'r1', 'r2', 'input_mc', 'input_none', 'input_m', 'input_c'
]
for i_m in xrange(len(self.m_plot)):
for i_c in xrange(len(self.c_plot)):
m = self.m_plot[i_m]
c = self.c_plot[i_c]
result = self.run_Newsome_model(p, m, c, biphasic_input=False)
for i_list in xrange(n_list):
record_list[i_list] = np.concatenate(
(record_list[i_list], result[name_list[i_list]]))
print 'm = %0.2f, c = %0.2f' % (m, c)
# z-score results
zscore_list = list()
for i_list in xrange(n_list):
zscore = record_list[i_list]
temp = zscore[zscore >
0.01] # exclude the data before and after dots
#zscore -= np.mean(record_list[i_list])
zscore = zscore / np.std(temp)
zscore_list.append(zscore)
zs = zscore_list
Dim_dec = -(zs[0] - zs[1]) / 2
Dim_motion = (zs[2] - zs[3] + zs[4] - zs[5]) / 4
Dim_color = (zs[2] - zs[3] - zs[4] + zs[5]) / 4
N_t = len(result['r1'])
maxr = max(
[max(abs(Dim_dec)),
max(abs(Dim_motion)),
max(abs(Dim_color))])
smalllinewidth = 0.5
largelinewidth = 1
mks = 2
n_m = 6
n_c = 6
px = [0] * n_m
py = [0] * n_m
for i_cond in xrange(n_m * n_c):
i_motion = i_cond // n_m
px[i_motion] += Dim_dec[i_cond * N_t:(i_cond + 1) * N_t] / n_c
py[i_motion] += Dim_motion[i_cond * N_t:(i_cond + 1) * N_t] / n_c
dotoffcolor = np.array([84, 39, 143]) / 255.
fig = MyFigure(figsize=(3, 1.5))
pl = fig.addplot(rect=[0.01, 0.05, 0.45, 0.9])
for i_motion in xrange(n_m):
if i_motion < (n_m / 2):
mfc = 'white'
linewidth = smalllinewidth
linecolor = motion_colors[i_motion]
mec = linecolor
else:
linecolor = motion_colors[n_m - i_motion - 1]
mfc = motion_colors[n_m - i_motion - 1]
linewidth = largelinewidth
mec = mfc
pl.plot([px[i_motion][-1]] * 2, [py[i_motion][-1], 0],
'o-',
color=dotoffcolor,
markerfacecolor=dotoffcolor,
markeredgecolor=dotoffcolor,
linewidth=linewidth,
markersize=mks)
pl.plot(px[i_motion],
py[i_motion],
'o-',
color=linecolor,
markerfacecolor=mfc,
markeredgecolor=mec,
linewidth=linewidth,
markersize=mks)
#xlabel('Dim Dec')
#ylabel('Dim Motion')
pylab.xlim((-maxr, maxr))
pylab.ylim((-maxr, maxr))
pylab.axis('equal')
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
px_1 = [0] * n_c
py_1 = [0] * n_c
px_2 = [0] * n_c
py_2 = [0] * n_c
for i_cond in xrange(n_m * n_c):
i_color = np.mod(i_cond, n_c)
i_motion = i_cond // n_m
if i_motion < (n_m / 2):
px_1[i_color] += Dim_dec[i_cond * N_t:(i_cond + 1) *
N_t] / n_c * 2
py_1[i_color] += Dim_color[i_cond * N_t:(i_cond + 1) *
N_t] / n_c * 2
else:
px_2[i_color] += Dim_dec[i_cond * N_t:(i_cond + 1) *
N_t] / n_c * 2
py_2[i_color] += Dim_color[i_cond * N_t:(i_cond + 1) *
N_t] / n_c * 2
pl = fig.addplot(rect=[0.54, 0.05, 0.45, 0.9])
for i_color in xrange(n_c):
if i_color < (n_c / 2):
linecolor = color_colors[i_color]
mfc = 'white'
mec = linecolor
else:
linecolor = color_colors[n_c - i_color - 1]
mfc = linecolor
mec = mfc
pl.plot([px_1[i_color][-1]] * 2, [py_1[i_color][-1], 0],
'o-',
color=dotoffcolor,
markerfacecolor=dotoffcolor,
markeredgecolor=dotoffcolor,
linewidth=smalllinewidth,
markersize=mks)
pl.plot([px_2[i_color][-1]] * 2, [py_2[i_color][-1], 0],
'o-',
color=dotoffcolor,
markerfacecolor=dotoffcolor,
markeredgecolor=dotoffcolor,
linewidth=largelinewidth,
markersize=mks)
pl.plot(px_1[i_color],
py_1[i_color],
'o-',
color=linecolor,
markerfacecolor=mfc,
markeredgecolor=mec,
linewidth=smalllinewidth,
markersize=mks)
pl.plot(px_2[i_color],
py_2[i_color],
'o-',
color=linecolor,
markerfacecolor=mfc,
markeredgecolor=mec,
linewidth=largelinewidth,
markersize=mks)
#xlabel('Dim Dec')
#ylabel('Dim Color')
pylab.xlim((-maxr, maxr))
pylab.ylim((-maxr, maxr))
pl.ax.set_frame_on(False)
pl.ax.set_yticks([])
pl.ax.set_xticks([])
if savenew:
fig.save(self.figpath + 'plot_Newsome_statespace' + '_' +
self.monkey + self.version)
def plot_WeightChangevsrE(self):
'''
Plot weight change when varying excitatory input rate
'''
with open(self.datapath+'resultlist_varyrate_postr10','rb') as f:
result_list = pickle.load(f)
with open(self.datapath+'learning_para_2014-11-10_0','rb') as f:
para = pickle.load(f)
q = para.copy()
# in vivo change (see Higgins, Graupner, Brunel 2014)
#q['NMDA_scaling'] = q['NMDA_scaling']*1.5/2
#q['bAP_scaling'] = q['bAP_scaling']/5
vDend_mean_list = list()
w_post_list = list()
AlphaP_list = list()
AlphaD_list = list()
for result in result_list:
p = result['params']
vDend = result['vDend']
NMDACasyn = result['NMDACasyn']
bAPCa = result['bAPCa']
vDend_mean = vDend.mean(axis=1)
vDend_mean_list.append(vDend_mean)
num_syn = NMDACasyn.shape[0]
num_soma = bAPCa.shape[0]
synsomaratio = num_syn//num_soma
bAPCasyn = bAPCa.repeat(synsomaratio,axis=0)
CaTrace = NMDACasyn*q['NMDA_scaling'] + bAPCasyn*q['bAP_scaling']
w_post = zeros(num_syn)
AlphaP_plot = zeros(num_syn)
AlphaD_plot = zeros(num_syn)
n_eachrate = num_syn//p['n_rate']
repeat_times = 5
record_dt = p['record_dt']
for i_syn in xrange(num_syn):
CaTraceSorted = sort(CaTrace[i_syn])
AlphaP = MCM.crossthresholdtime(CaTraceSorted,q['thetaP'])*record_dt*repeat_times
AlphaD = MCM.crossthresholdtime(CaTraceSorted,q['thetaD'])*record_dt*repeat_times
wpre = 1
wpost_syn = MCM.SynapseChange(wpre,AlphaP,AlphaD,q)
w_post[i_syn] = wpost_syn
AlphaP_plot[i_syn] = AlphaP
AlphaD_plot[i_syn] = AlphaD
w_post = w_post.reshape(p['n_rate'],n_eachrate)
w_post = w_post.mean(axis=1)
w_post_list.append(w_post)
AlphaP_list.append(AlphaP_plot)
AlphaD_list.append(AlphaD_plot)
dend_inh_rates = p['dend_inh_rates']
colorMatrix = array([[65,182,196],
[34,94,168],
[8,29,88]])/255
fig=MyFigure(figsize=(2.5,2.5))
pl = fig.addplot(rect=[0.2,0.25,0.7,0.7])
plotlist = list()
i = 0
for vDend_mean in vDend_mean_list:
ax, = pl.plot(p['pre_rates'],vDend_mean/mV,color=colorMatrix[i,:])
plotlist.append(ax)
xlabel('Pre-synaptic rate (Hz)')
ylabel('Mean Dendritic Voltage (mV)')
i += 1
pl.ax.set_xticks([0,20,40])
pl.ax.set_ylim((-70,-10))
pl.ax.set_yticks([-70,-50,-30,-10])
leg=legend(plotlist,['%d' % r for r in dend_inh_rates],title='Inhibition (Hz)',loc=2)
leg.draw_frame(False)
#fig.save('figures/RateVsInhibition'+self.version)
fig=MyFigure(figsize=(2,2))
pl = fig.addplot(rect=[0.2,0.2,0.7,0.7])
pl.plot([0,max(p['pre_rates'])],[1,1],color=array([189,189,189])/255)
i = 0
plotlist = list()
for w_post in w_post_list:
ax, = pl.plot(p['pre_rates'],w_post,color=colorMatrix[i,:])
plotlist.append(ax)
xlabel('Pre-synaptic rate (Hz)')
ylabel('Weight change')
i += 1
pl.ax.set_xticks([0,20,40])
pl.ax.set_ylim((0,3))
pl.ax.set_yticks([0,1,2,3])
leg=legend(plotlist,['%d' % r for r in dend_inh_rates],title='Inhibition (Hz)',loc=2)
leg.draw_frame(False)
#title('post rate = %d Hz' % p['post_rate'])
fig.save(self.figpath+'WeightChangevsrE_postr%d' % p['post_rate']+self.version)
def plot_EvsDI(self,
name='NMDA',
denseinh=False,
bothinh=True,
rate_range=60):
'''
plot mean dendritic voltage as a function of excitatory input rate,
and inhibitory rate
'''
if denseinh:
di = 'denseinh_'
k = 10
else:
di = ''
k = 1
name_append = di + name + ('_range%d' % rate_range) + self.version
with open(
self.datapath + 'EvsDI_' + di + name +
('_range%d' % rate_range) + '_latest.pkl', 'rb') as f:
res = pickle.load(f)
if rate_range > 100:
figsize = (1.5, 1.3)
ylim = (-70, 0)
yticks = [-70, -10]
rect = [0.25, 0.3, 0.65, 0.65]
else:
figsize = (1.3, 1.3)
ylim = (-70, -10)
yticks = [-70, -20]
rect = [0.3, 0.3, 0.65, 0.65]
fig = MyFigure(figsize=figsize)
pl = fig.addplot(rect=rect)
pl.plot(res['pre_rates'],
res['vDend_mean'][0],
color=colorMatrix[1],
label='0')
print res['vDend_mean'][0]
if bothinh:
pl.plot(res['pre_rates'],
res['vDend_mean'][30],
color=colorMatrix[5],
label='30')
#leg=legend(title='Inhibition (Hz)',loc=2, bbox_to_anchor=[-0.05, 1.05],frameon=False)
pl.ax.set_ylim(ylim)
pl.ax.set_yticks(yticks)
xticks = [0, int(rate_range / 2), rate_range]
pl.ax.set_xticks(xticks)
#xlabel('Rate of %d ' % p['num_inputeach']+name+' inputs (Hz)' )
xlabel('Rate (Hz)')
if name is 'NMDA':
ylabel('$\overline{V}_D$ (mV)', labelpad=-5)
else:
pl.ax.set_yticklabels(['', ''])
if bothinh:
fig.save(self.figpath + 'EvsDI_' + name_append)
else:
fig.save(self.figpath + 'EvsDI_preinh_' + name_append)
if name is 'NMDA':
inh_list = res['vDend_mean'].keys()
inh_list = sort(inh_list)
fig = MyFigure(figsize=(2.2, 1.6))
pl = fig.addplot(rect=[0.25, 0.25, 0.7, 0.7])
for i in xrange(len(inh_list)):
color = colorMatrix[i + 1, :]
pl.plot(res['pre_rates'],
res['vDend_mean'][inh_list[i]],
color=color,
label='%d' % (inh_list[i] * k))
leg = legend(title='Inhibition (Hz)',
loc=2,
bbox_to_anchor=[0.02, 1.05],
frameon=False)
pl.ax.set_ylim((-70, -10))
pl.ax.set_yticks([-70, -20])
pl.ax.set_xticks(xticks)
#xlabel('Rate of %d ' % p['num_inputeach']+name+' inputs (Hz)' )
xlabel('Excitatory input rate (Hz)')
ylabel('Mean dendritic voltage (mV)', labelpad=5)
fig.save(self.figpath + 'EvsDI_full_' + name_append)
def plot_compare(self,p,savenew=True,savename=''):
cohs = [0.05,0.15,0.5]
x_plot = [-coh for coh in cohs[::-1]]+cohs
x_plot = np.array(x_plot)
p_frac1 = p.copy()
p_frac1['frac_proj'] = 1
r_mc_list = np.zeros((6,6))
for i_m in xrange(len(x_plot)):
for i_c in xrange(len(x_plot)):
m = x_plot[i_m]
c = x_plot[i_c]
#for a particular value of motion and color m,c \in (-1,1)
#the input from the two pathways to the four considered populations are
#(m,c)
#(-m,c)
#(m,-c)
#(-m,-c)
r_mc_list[i_m,i_c] = self.analytic_twopath(p_frac1,40*(1+m),40*(1+c))
input_plot_all = r_mc_list - r_mc_list[::-1,::-1]
win1_list = self.perform_func(input_plot_all)
n_c = n_m = 6
Dim_motion = r_mc_list + r_mc_list[:,::-1] - r_mc_list[::-1,:] - r_mc_list[::-1,::-1]
Dim_color = r_mc_list - r_mc_list[:,::-1] + r_mc_list[::-1,:] - r_mc_list[::-1,::-1]
smalllinewidth = 1
largelinewidth = 2.5
Dim_motion = Dim_motion.mean(axis=1)
fig=MyFigure(figsize=(4,4))
pl = fig.addplot(rect=[0.2,0.2,0.7,0.7])
for i_motion in xrange(n_m):
if i_motion<(n_m/2):
facecolor = 'white'
linewidth = smalllinewidth
linecolor = motion_colors[i_motion]
px = 1
else:
linecolor = motion_colors[n_m-i_motion-1]
facecolor = motion_colors[n_m-i_motion-1]
linewidth = largelinewidth
px = -1
pl.plot(px,Dim_motion[i_motion],
'o',markeredgecolor=linecolor,
markerfacecolor=facecolor,linewidth=linewidth)
Dim_color1 = Dim_color[:3,:].mean(axis=0)
Dim_color2 = Dim_color[3:,:].mean(axis=0)
print Dim_motion
print Dim_color1
for i_color in xrange(n_c):
if i_color<(n_c/2):
linecolor = color_colors[i_color]
markerfacecolor = 'white'
else:
linecolor = color_colors[n_c-i_color-1]
markerfacecolor = linecolor
pl.plot(0.5,Dim_color1[i_color],'o',
markeredgecolor=linecolor,markerfacecolor=markerfacecolor,linewidth=smalllinewidth)
pl.plot(-0.5,Dim_color2[i_color],'o',
markeredgecolor=linecolor,markerfacecolor=markerfacecolor,linewidth=largelinewidth)
pylab.xlabel('Dim Dec')
pylab.ylabel('Dim Motion/Color')
self.quick_runNewsome_psychometric(p,savenew=savenew,savename=savename)