def make_2models_figure(ax, MODEL1, MODEL2, I0=300e-12,\
color1='r', color2='b'):
params = models.get_model_params(MODEL1, {})
Inorm = np.array([1. if (tt>100e-3 and tt<500e-3) else 0 for tt in t])
if params.has_key('Istep'):
I0 = params['Istep']
I = I0*Inorm
if (MODEL1.split('-')[0]=='iAdExp') or (MODEL1.split('-')[0]=='iLIF'):
v1, va1, spikes1 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL1, full_I_traces=I, return_threshold=True)
v2, va2, spikes2 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL2, full_I_traces=I, return_threshold=True)
else:
v1, spikes1 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL1, full_I_traces=I)
v2, spikes2 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL2, full_I_traces=I)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.plot(1e3*t, 1e3*v1, color=color1)
ax.plot(1e3*t, -85+10*Inorm, 'k')
ax.plot(1e3*t, 1e3*v2+30, color=color2)
if (MODEL1.split('-')[0]=='iAdExp') or (MODEL1.split('-')[0]=='iLIF'):
ax.plot(1e3*t, 1e3*va1, 'k:')
ax.plot(1e3*t, 1e3*va2+30, 'k:')
ax.plot([1e3*t[0], 1e3*t[0]], [1e3*v1[0], 1e3*v2[0]+30], 'k>', ms=10)
ax.annotate('-70mV', (.1,.3), xycoords='axes fraction')
# plt.tight_layout()
ax.plot([10,10],[-25,-15], 'gray', lw=3)
ax.plot([10,60],[-25,-25], 'gray', lw=3)
ax.annotate('10mV', (16,-10), textcoords='data', size=13)
ax.annotate(str(int(abs(1e12*I0)))+'pA', (16,-20), textcoords='data',size=13)
ax.annotate('50ms', (17,-40), textcoords='data', size=13)
ax.annotate('-70mV', (-10, -70), textcoords='data', size=13)
def make_2models_figure(ax, MODEL1, MODEL2, I0=300e-12, color1="r", color2="b"):
params = models.get_model_params(MODEL1, {})
Inorm = np.array([1.0 if (tt > 100e-3 and tt < 500e-3) else 0 for tt in t])
if params.has_key("Istep"):
I0 = params["Istep"]
I = I0 * Inorm
if (MODEL1.split("-")[0] == "iAdExp") or (MODEL1.split("-")[0] == "iLIF"):
v1, va1, spikes1 = single_experiment(
t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL1, full_I_traces=I, return_threshold=True
)
v2, va2, spikes2 = single_experiment(
t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL2, full_I_traces=I, return_threshold=True
)
else:
v1, spikes1 = single_experiment(t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL1, full_I_traces=I)
v2, spikes2 = single_experiment(t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL2, full_I_traces=I)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.plot(1e3 * t, 1e3 * v1, color=color1)
ax.plot(1e3 * t, -85 + 10 * Inorm, "k")
ax.plot(1e3 * t, 1e3 * v2 + 30, color=color2)
if (MODEL1.split("-")[0] == "iAdExp") or (MODEL1.split("-")[0] == "iLIF"):
ax.plot(1e3 * t, 1e3 * va1, "k:")
ax.plot(1e3 * t, 1e3 * va2 + 30, "k:")
ax.plot([1e3 * t[0], 1e3 * t[0]], [1e3 * v1[0], 1e3 * v2[0] + 30], "k>", ms=10)
ax.annotate("-70mV", (0.1, 0.3), xycoords="axes fraction")
# plt.tight_layout()
ax.plot([10, 10], [-25, -15], "gray", lw=3)
ax.plot([10, 60], [-25, -25], "gray", lw=3)
ax.annotate("10mV", (16, -10), textcoords="data", size=13)
ax.annotate(str(int(abs(1e12 * I0))) + "pA", (16, -20), textcoords="data", size=13)
ax.annotate("50ms", (17, -40), textcoords="data", size=13)
ax.annotate("-70mV", (-10, -70), textcoords="data", size=13)
def make_model_figure(MODEL, I0=100e-12,
savefig=False, for_title=None):
params = models.get_model_params(MODEL, {})
Inorm = np.array([1. if (tt>100e-3 and tt<500e-3) else 0 for tt in t])
if params.has_key('Istep'):
I0 = params['Istep']
I = I0*Inorm
fig = plt.figure(figsize=(6,4))
ax = plt.subplot(111, frameon=False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.title(params['name']+'\n', fontsize=25)
if MODEL.split('-')[0]=='aEIF':
[v1, va1], spikes = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=I)
[v2, va2], spikes2 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=-I)
plt.plot(1e3*t, 1e3*v1, 'k', label='soma')
plt.plot(1e3*t, 1e3*va1, 'r', label='axon')
plt.plot(1e3*t, -85+10*Inorm, 'k')
plt.plot(1e3*t, 1e3*v2, 'k--')
plt.plot(1e3*t, 1e3*va2, 'r--')
plt.plot(1e3*t, -85-10*Inorm, 'k--')
plt.legend(loc='upper right', frameon=False)
elif MODEL.split('-')[0]=='iLIF' or MODEL.split('-')[0]=='iAdExp':
v1, va1, spikes = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=I, return_threshold=True)
v2, va2, spikes2 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=-I, return_threshold=True)
plt.plot(1e3*t, 1e3*v1, 'k', label=r'$V_\mathrm{m}$')
plt.plot(1e3*t, 1e3*va1, 'r', label=r'$\theta$')
plt.plot(1e3*t, -85+10*Inorm, 'k')
plt.plot(1e3*t, 1e3*v2, 'k--')
plt.plot(1e3*t, -85-10*Inorm, 'k--')
plt.legend(loc='upper right', frameon=False)
else:
v1, spikes = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=I)
v2, spikes2 = single_experiment(t, 0., 0., 0., 0., 1e9, -70e-3, params,\
MODEL=MODEL, full_I_traces=-I)
plt.plot(1e3*t, 1e3*v1, 'k')
plt.plot(1e3*t, -85+10*Inorm, 'k')
plt.plot(1e3*t, 1e3*v2, 'k--')
plt.plot(1e3*t, -85-10*Inorm, 'k--')
if MODEL is not 'Wang-Buszaki':
for s in spikes:
plt.plot([1e3*s,1e3*s], [5e3*params['delta_v']+\
1e3*params['vthresh'],20], 'k:')
for s in spikes2:
plt.plot([1e3*s,1e3*s], [5e3*params['delta_v']+\
1e3*params['vthresh'],20], 'k:')
plt.tight_layout()
plt.plot([10,10],[-25,-15], 'gray', lw=3)
plt.plot([10,60],[-25,-25], 'gray', lw=3)
plt.annotate('10mV', (16,-10), textcoords='data', size=13)
plt.annotate(str(int(1e12*I0))+'pA', (16,-20), textcoords='data',size=13)
plt.annotate('50ms', (17,-40), textcoords='data', size=13)
if savefig==True:
fig.savefig('../figures/'+MODEL+'_step_response.svg',\
format='svg', transparent=True)
return fig
def make_model_figure(MODEL, I0=100e-12, savefig=False, for_title=None):
params = models.get_model_params(MODEL, {})
Inorm = np.array([1.0 if (tt > 100e-3 and tt < 500e-3) else 0 for tt in t])
if params.has_key("Istep"):
I0 = params["Istep"]
I = I0 * Inorm
fig = plt.figure(figsize=(6, 4))
ax = plt.subplot(111, frameon=False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.title(params["name"] + "\n", fontsize=25)
if MODEL.split("-")[0] == "aEIF":
[v1, va1], spikes = single_experiment(t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=I)
[v2, va2], spikes2 = single_experiment(
t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=-I
)
plt.plot(1e3 * t, 1e3 * v1, "k", label="soma")
plt.plot(1e3 * t, 1e3 * va1, "r", label="axon")
plt.plot(1e3 * t, -85 + 10 * Inorm, "k")
plt.plot(1e3 * t, 1e3 * v2, "k--")
plt.plot(1e3 * t, 1e3 * va2, "r--")
plt.plot(1e3 * t, -85 - 10 * Inorm, "k--")
plt.legend(loc="upper right", frameon=False)
elif MODEL.split("-")[0] == "iLIF" or MODEL.split("-")[0] == "iAdExp":
v1, va1, spikes = single_experiment(
t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=I, return_threshold=True
)
v2, va2, spikes2 = single_experiment(
t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=-I, return_threshold=True
)
plt.plot(1e3 * t, 1e3 * v1, "k", label=r"$V_\mathrm{m}$")
plt.plot(1e3 * t, 1e3 * va1, "r", label=r"$\theta$")
plt.plot(1e3 * t, -85 + 10 * Inorm, "k")
plt.plot(1e3 * t, 1e3 * v2, "k--")
plt.plot(1e3 * t, -85 - 10 * Inorm, "k--")
plt.legend(loc="upper right", frameon=False)
else:
v1, spikes = single_experiment(t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=I)
v2, spikes2 = single_experiment(t, 0.0, 0.0, 0.0, 0.0, 1e9, -70e-3, params, MODEL=MODEL, full_I_traces=-I)
plt.plot(1e3 * t, 1e3 * v1, "k")
plt.plot(1e3 * t, -85 + 10 * Inorm, "k")
plt.plot(1e3 * t, 1e3 * v2, "k--")
plt.plot(1e3 * t, -85 - 10 * Inorm, "k--")
if MODEL is not "Wang-Buszaki":
for s in spikes:
plt.plot([1e3 * s, 1e3 * s], [5e3 * params["delta_v"] + 1e3 * params["vthresh"], 20], "k:")
for s in spikes2:
plt.plot([1e3 * s, 1e3 * s], [5e3 * params["delta_v"] + 1e3 * params["vthresh"], 20], "k:")
plt.tight_layout()
plt.plot([10, 10], [-25, -15], "gray", lw=3)
plt.plot([10, 60], [-25, -25], "gray", lw=3)
plt.annotate("10mV", (16, -10), textcoords="data", size=13)
plt.annotate(str(int(1e12 * I0)) + "pA", (16, -20), textcoords="data", size=13)
plt.annotate("50ms", (17, -40), textcoords="data", size=13)
if savefig == True:
fig.savefig("../figures/" + MODEL + "_step_response.svg", format="svg", transparent=True)
return fig
def make_simulation_for_model(MODEL, args, return_output=False,\
sampling='low'):
if sampling is 'low':
# discretization and seed
SEED = np.arange(2)+1
dt, tstop = 1e-4, 2.
else:
SEED = np.arange(3)+1
dt, tstop = 1e-5, 10.
params = models.get_model_params(MODEL, {}) # paramters of the model, see models.py
### PARAMETERS OF THE EXPERIMENT
if len(args.RANGE_FOR_3D)>1:
params['RANGE_FOR_3D'] = args.RANGE_FOR_3D
muV_min, muV_max, sV_min1, sV_max1, sV_min2, sV_max2, Ts_ratio = params['RANGE_FOR_3D']
muGn_min, muGn_max = 1.15, 8.
### cell and synaptic parameters, see models.py !!
sim_params = {'dt':dt, 'tstop':tstop}
t_long = np.arange(0,int(tstop/dt))*dt
muV = np.linspace(muV_min, muV_max, args.DISCRET_muV, endpoint=True)
# trying with the linear autocorrelation
args.DISCRET_muG = args.DISCRET_TvN
Tv_ratio = np.linspace(1./muGn_max+Ts_ratio, 1./muGn_min+Ts_ratio, args.DISCRET_muG, endpoint=True)
muGn = 1./(Tv_ratio-Ts_ratio)
muV, sV, muGn = np.meshgrid(muV, np.zeros(args.DISCRET_sV), muGn)
Tv_ratio = Ts_ratio+1./muGn
for i in range(args.DISCRET_muV):
sv1 = sV_min1+i*(sV_min2-sV_min1)/(args.DISCRET_muV-1)
sv2 = sV_max1+i*(sV_max2-sV_max1)/(args.DISCRET_muV-1)
for l in range(args.DISCRET_muG):
sV[:,i,l] = np.linspace(sv1,sv2,args.DISCRET_sV,endpoint=True)
I0, Gs, f, Q, Ts = params_variations_calc(muGn,muV,sV,Ts_ratio*np.ones(muGn.shape),params)
Fout = np.zeros((args.DISCRET_sV, args.DISCRET_muV, args.DISCRET_muG, len(SEED)))
# measuring the subthreshold fluctuations properties
muV_exp, sV_exp, TvN_exp = 0*Fout, 0*Fout, 0*Fout
for i_muV in range(args.DISCRET_muV):
print '[[[[]]]]=====> muV : ', round(1e3*muV[0, i_muV, 0],1), 'mV'
for i_sV in range(args.DISCRET_sV):
print '[[[]]]====> sV : ', round(1e3*sV[i_sV, i_muV, 0],1), 'mV'
for ig in range(args.DISCRET_muG):
print '[]=> muGn : ', round(muGn[i_sV, i_muV, ig],1),\
'TvN : ', round(100*Tv_ratio[i_sV, i_muV, ig],1), '%'
for i_s in range(len(SEED)):
v, spikes = single_experiment(\
t_long, I0[i_sV, i_muV, ig],\
Gs[i_sV, i_muV, ig],\
f[i_sV, i_muV, ig],\
Q[i_sV, i_muV, ig],\
Ts[i_sV, i_muV, ig],\
muV[i_sV, i_muV, ig],\
params, MODEL=MODEL,
seed=SEED[i_s]+i_muV+i_sV+ig)
Fout[i_sV, i_muV, ig, i_s] =\
len(spikes)/t_long.max()
muV_exp[i_sV, i_muV, ig, i_s],\
sV_exp[i_sV, i_muV, ig, i_s],\
TvN_exp[i_sV, i_muV, ig, i_s] = \
measuring_subthre_dynamics(v, spikes, dt,\
Tm0=params['Cm']/params['Gl'])
data_path = '../data/'+MODEL+'_'+args.SUFFIX_NAME+'.npz'
D = dict(muV=1e3*muV.flatten(), sV=1e3*sV.flatten(),\
TvN=Ts_ratio+1./muGn.flatten(),\
muGn=muGn.flatten(),\
Fout=Fout.mean(axis=-1).flatten(),\
s_Fout=Fout.std(axis=-1).flatten(),\
muV_exp=1e3*muV_exp.mean(axis=-1).flatten(),\
sV_exp=1e3*sV_exp.mean(axis=-1).flatten(),\
TvN_exp=TvN_exp.mean(axis=-1).flatten(),\
s_muV_exp=1e3*muV_exp.std(axis=-1).flatten(),\
s_sV_exp=1e3*sV_exp.std(axis=-1).flatten(),\
s_TvN_exp=TvN_exp.std(axis=-1).flatten(),\
MODEL=MODEL,\
Gl=params['Gl'], Cm=params['Cm'], El=params['El'])
np.savez(data_path,**D)
def make_simulation_for_model(MODEL, args, return_output=False,\
sampling='low'):
if sampling is 'low':
# discretization and seed
SEED = np.arange(2) + 1
dt, tstop = 1e-4, 2.
else:
SEED = np.arange(3) + 1
dt, tstop = 1e-5, 10.
params = models.get_model_params(
MODEL, {}) # paramters of the model, see models.py
### PARAMETERS OF THE EXPERIMENT
if len(args.RANGE_FOR_3D) > 1:
params['RANGE_FOR_3D'] = args.RANGE_FOR_3D
muV_min, muV_max, sV_min1, sV_max1, sV_min2, sV_max2, Ts_ratio = params[
'RANGE_FOR_3D']
muGn_min, muGn_max = 1.15, 8.
### cell and synaptic parameters, see models.py !!
sim_params = {'dt': dt, 'tstop': tstop}
t_long = np.arange(0, int(tstop / dt)) * dt
muV = np.linspace(muV_min, muV_max, args.DISCRET_muV, endpoint=True)
# trying with the linear autocorrelation
args.DISCRET_muG = args.DISCRET_TvN
Tv_ratio = np.linspace(1. / muGn_max + Ts_ratio,
1. / muGn_min + Ts_ratio,
args.DISCRET_muG,
endpoint=True)
muGn = 1. / (Tv_ratio - Ts_ratio)
muV, sV, muGn = np.meshgrid(muV, np.zeros(args.DISCRET_sV), muGn)
Tv_ratio = Ts_ratio + 1. / muGn
for i in range(args.DISCRET_muV):
sv1 = sV_min1 + i * (sV_min2 - sV_min1) / (args.DISCRET_muV - 1)
sv2 = sV_max1 + i * (sV_max2 - sV_max1) / (args.DISCRET_muV - 1)
for l in range(args.DISCRET_muG):
sV[:, i, l] = np.linspace(sv1, sv2, args.DISCRET_sV, endpoint=True)
I0, Gs, f, Q, Ts = params_variations_calc(muGn, muV, sV,
Ts_ratio * np.ones(muGn.shape),
params)
Fout = np.zeros(
(args.DISCRET_sV, args.DISCRET_muV, args.DISCRET_muG, len(SEED)))
# measuring the subthreshold fluctuations properties
muV_exp, sV_exp, TvN_exp = 0 * Fout, 0 * Fout, 0 * Fout
for i_muV in range(args.DISCRET_muV):
print '[[[[]]]]=====> muV : ', round(1e3 * muV[0, i_muV, 0], 1), 'mV'
for i_sV in range(args.DISCRET_sV):
print '[[[]]]====> sV : ', round(1e3 * sV[i_sV, i_muV, 0], 1), 'mV'
for ig in range(args.DISCRET_muG):
print '[]=> muGn : ', round(muGn[i_sV, i_muV, ig],1),\
'TvN : ', round(100*Tv_ratio[i_sV, i_muV, ig],1), '%'
for i_s in range(len(SEED)):
v, spikes = single_experiment(\
t_long, I0[i_sV, i_muV, ig],\
Gs[i_sV, i_muV, ig],\
f[i_sV, i_muV, ig],\
Q[i_sV, i_muV, ig],\
Ts[i_sV, i_muV, ig],\
muV[i_sV, i_muV, ig],\
params, MODEL=MODEL,
seed=SEED[i_s]+i_muV+i_sV+ig)
Fout[i_sV, i_muV, ig, i_s] =\
len(spikes)/t_long.max()
muV_exp[i_sV, i_muV, ig, i_s],\
sV_exp[i_sV, i_muV, ig, i_s],\
TvN_exp[i_sV, i_muV, ig, i_s] = \
measuring_subthre_dynamics(v, spikes, dt,\
Tm0=params['Cm']/params['Gl'])
data_path = '../data/' + MODEL + '_' + args.SUFFIX_NAME + '.npz'
D = dict(muV=1e3*muV.flatten(), sV=1e3*sV.flatten(),\
TvN=Ts_ratio+1./muGn.flatten(),\
muGn=muGn.flatten(),\
Fout=Fout.mean(axis=-1).flatten(),\
s_Fout=Fout.std(axis=-1).flatten(),\
muV_exp=1e3*muV_exp.mean(axis=-1).flatten(),\
sV_exp=1e3*sV_exp.mean(axis=-1).flatten(),\
TvN_exp=TvN_exp.mean(axis=-1).flatten(),\
s_muV_exp=1e3*muV_exp.std(axis=-1).flatten(),\
s_sV_exp=1e3*sV_exp.std(axis=-1).flatten(),\
s_TvN_exp=TvN_exp.std(axis=-1).flatten(),\
MODEL=MODEL,\
Gl=params['Gl'], Cm=params['Cm'], El=params['El'])
np.savez(data_path, **D)
