frequenciesAll, maxFacilitationAll = simulate_steady_state_freq(numpy.arange(1.,100.,1.),
flag='max')
# Recovery spike
revoceryTimes, relativeRecovery = simulate_recovery(numpy.arange(100,5000,200))
# Steady state conductance 0-1 frequencies
frequenciesZoom, relativeFacilitationZoom = simulate_steady_state_freq(numpy.arange(0.1,1.,.1))
# DISPLAY
plot_settings.set_mode(mode='by_fontsize', w = 750.0, h = 400.0, fontsize=12)
font_size_text = 9
fig = pylab.figure( facecolor = 'w' )
ax_list = []
ax_list.append( MyAxes(fig, [ .1, .4, .18, .26 ] ) ) # text box
ax_list.append( MyAxes(fig, [ .35, .6, .24, .34 ] ) ) #
ax_list.append( MyAxes(fig, [ .73, .6, .24, .34 ] ) ) #
ax_list.append( MyAxes(fig, [ .35, .1, .24, .34 ] ) ) #
ax_list.append( MyAxes(fig, [ .73, .1, .24, .34 ] ) ) #
# Text
ax=ax_list[0]
plot_text(ax)
# Example steady state all
ax=ax_list[1]
plot_steady_state_freq(ax, frequenciesAll, steadyStateFacilitationAll)
# Example steady state zoom
ax=ax_list[2]
save_result_at)
else:
print 'hej'
#nbNeurons1, meanRates1, meanRatesStd1, info_string = misc.pickle_load(save_result_at)
# DISPLAY
plot_settings.set_mode(pylab,
mode='by_fontsize',
w=1100.0,
h=450.0,
fontsize=12)
font_size_text = 8
fig = pylab.figure(facecolor='w')
ax_list = []
ax_list.append(MyAxes(fig, [.075, .37, .135, .26])) # text box
ax_list.append(MyAxes(fig, [.26, .6, .165, .34])) #
ax_list.append(MyAxes(fig, [.53, .6, .165, .34])) #
ax_list.append(MyAxes(fig, [.26, .1, .165 * 2.312, .34])) #
ax_list.append(MyAxes(fig, [.8, .6, .165, .34])) #
#ax_list.append( MyAxes(fig, [ .53, .1, .165, .34 ] ) ) #
ax_list.append(MyAxes(fig, [.8, .1, .165, .34])) #
# Text
ax = ax_list[0]
plot_text(ax, info_string)
ax = ax_list[1]
plot_example_raster_MSN(ax, MSN_spikes_and_ids)
ax = ax_list[2]
start_rec )
names=names[0:15]
model_params['conns'].update({'STN_SNR':{'syn':'STN_SNR_ampa_p3'}})
save_result_at=OUTPUT_PATH+'/simulate_sensitivity_STN_plast.plk'
d2, data_rate_change2, dp2, names2=simulate_sensitivity([1, 1], save_result_at,n_exp,
params_msn_d1, params_msn_d2, params_stn,
synapse_models, model_params, sim_time,
start_rec )
#Inspect results
plot_settings.set_mode(pylab, mode='by_fontsize', w = 1100.0, h = 450.0+275.0, fontsize=16)
font_size_text = 8
fig = pylab.figure( facecolor = 'w' )
ax_list = []
ax_list.append( MyAxes(fig, [ .075, .37, .135, .26 ] ) ) # text box
ax_list.append( MyAxes(fig, [ .26, .7, .4, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .53, .7, .165, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .8, .7, .165, .2 ] ) ) #
ax_list.append( MyAxes(fig, [ .26, .3, .4, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .53, .4, .165, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .8, .4, .165, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .26, .1, .165, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .53, .1, .165, .2 ] ) ) #
#ax_list.append( MyAxes(fig, [ .8, .1, .165, .2 ] ) ) #
ax=ax_list[1]
plot_data(ax, data_rate_change[0], 'Static STN-SNr', names, show_label=False)
ax=ax_list[1]
my_nest.Connect(sgs, [MSN[0]], model=syn_model) # connect MSNs
T = 2000 # simulation time
my_nest.Simulate(T) # simulate
MSN.get_signal('v', 'V_m')
pylab.close('all') # display
# Create figure where figsize(width,height) and figure dimenstions window
# width = figsize(width) x dpi and window hight = figsize(hight) x dpi
plot_settings.set_mode(mode='dynamic', w=700.0, h=400.0)
font_size_text = 10
fig = pylab.figure(facecolor='w')
pylab.suptitle('MSN to MSN')
ax_list = []
ax_list.append(MyAxes(fig, [.1, .37, .2, .26])) # text box
ax_list.append(MyAxes(fig, [.50, .15, .40, .65])) # voltage trace
ds = my_nest.GetDefaults(syn_model)
sn = my_nest.GetStatus(MSN[:])[0]
ax = ax_list[0] # Text box
tb = ''
tb = tb + ' %6s %7s %3s \n' % ('Synapse model:', ds['synapsemodel'], ' ')
tb = tb + ' \n '
tb = tb + ' %6s %7s %3s \n' % ('Delay:', ds['delay'], 'ms')
tb = tb + ' %6s %7s %3s \n' % ('Weight:', ds['weight'], 'nS')
tb = tb + ' \n '
tb = tb + ' %6s %7s %3s \n' % ('Decay:', sn['GABAA_2_Tau_decay'], 'ms')
tb = tb + ' %6s %7s %3s \n' % ('E_rev:', sn['GABAA_2_E_rev'], 'mV')
#hzs=[0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 25.6, 51.2]
hzs = range(1, 48, 5)
signal_rates = simulate_signal_rates(load=True, hzs=hzs)
# DISPLAY
plot_settings.set_mode(pylab,
mode='by_fontsize',
w=1100.0,
h=450.0,
fontsize=12)
font_size_text = 8
fig = pylab.figure(facecolor='w')
ax_list = []
ax_list.append(MyAxes(fig, [.075, .37, .135, .26])) # text box
ax_list.append(MyAxes(fig, [.26, .6, .165, .34])) #
#ax_list.append( MyAxes(fig, [ .53, .6, .165, .34 ] ) ) #
#ax_list.append( MyAxes(fig, [ .8, .6, .165, .34 ] ) ) #
#ax_list.append( MyAxes(fig, [ .26, .1, .165, .34 ] ) ) #
#ax_list.append( MyAxes(fig, [ .53, .1, .165, .34 ] ) ) #
# ax_list.append( MyAxes(fig, [ .8, .1, .165, .34 ] ) ) #
# Text
ax = ax_list[0]
plot_text(ax, info_string)
ax = ax_list[1]
plot_signal_rates(ax, hzs, signal_rates)
pylab.show()
sim_time=20000
save_at=OUTPUT_PATH+'/simulate_network_fmin.plk'
x=fmin(0,save_at)
#x=[215, 0.08]
STN_target1, e1=restriction_1(gpe_rate, n_ctx, n_gpe, x, neuron_model, syn_models)
STN_target2, e2=restriction_2(gpe_rate, n_ctx, n_gpe, x, neuron_model, syn_models)
STN_target3 = GPE_46_hz(gpe_rate, n_ctx, n_gpe, x, neuron_model, syn_models)
plot_settings.set_mode(pylab, mode='by_fontsize', w = 500.0, h = 500.0, fontsize=8)
font_size_text = 8
fig = pylab.figure( facecolor = 'w')
ax_list=[]
ax_list.append( MyAxes(fig, [ .1, .7, .8, .2 ] ) ) # text box
ax_list.append( MyAxes(fig, [ .1, .4, .8, .2 ] ) ) #
ax_list.append( MyAxes(fig, [ .1, .1, .8, .2 ] ) ) #
ax=ax_list[0]
plot_example(ax, STN_target1,sim_time, x, type='No GPE')
ax=ax_list[1]
plot_example(ax, STN_target2, sim_time, x, type='Normal')
ax=ax_list[2]
plot_example(ax, STN_target3, sim_time, x, type='GPE rate at '+str(30*1.55))
pylab.show()
STN_example_rebound_spike_5, s = simulate_example_rebound_spike([-70.],
time=300.)
STN_example_rebound_spike_6, s = simulate_example_rebound_spike([-100.],
time=300.)
# DISPLAY
plot_settings.set_mode(pylab,
mode='by_fontsize',
w=1100.0,
h=450.0,
fontsize=16)
font_size_text = 8
fig = pylab.figure(facecolor='w')
ax_list = []
ax_list.append(MyAxes(fig, [.075, .37, .135, .26])) # text box
ax_list.append(MyAxes(fig, [.26, .6, .165, .34])) #
ax_list.append(MyAxes(fig, [.53, .6, .165, .34])) #
ax_list.append(MyAxes(fig, [.8, .6, .165, .075])) #
ax_list.append(MyAxes(fig, [.26, .3, .165, .075])) #
ax_list.append(MyAxes(fig, [.53, .3, .165, .075])) #
ax_list.append(MyAxes(fig, [.8, .3, .165, .075])) #
ax_list.append(MyAxes(fig, [.26, .1, .165, .075])) #
ax_list.append(MyAxes(fig, [.53, .1, .165, .075])) #
ax_list.append(MyAxes(fig, [.8, .1, .165, .075])) #
# Text
ax = ax_list[0]
plot_text(ax, infoString)
# Example
#ax=ax_list[1]
SNR_mean_rate.load_signal('s')
else:
SNR_example_plot.get_signal('v', 'V_m', stop=simTime)
SNR_mean_rate.get_signal('s', start=1, stop=simTime)
plot_settings.set_mode(mode='dynamic', w=1100.0, h=400.0)
font_size_text = 7
fig = pylab.figure(facecolor='w')
#fig = pylab.figure( figsize = ( 34, 12 ), dpi = 50, facecolor='w' )
nb_yticks_rp = 4 # number of ticks raster plots
nb_yticks_sr = 3 # number of ticks spike rate plots
binSize = 50 # spike rate bin
ax_list = []
ax_list.append(MyAxes(fig, [0.05, .37, .09, .26])) # text box
ax_list.append(MyAxes(fig, [0.22, .6, .2, .3])) # MSNs raster plot
ax_list.append(MyAxes(fig, [0.22, .12, .2, .3])) # MSNs rates
ax_list.append(MyAxes(fig, [0.57, .87, .2, .05])) # SNR trace plastic
ax_list.append(MyAxes(fig, [0.57, .81, .2, .05])) # SNR trace static w
ax_list.append(MyAxes(fig, [0.57, .74, .2, .05])) # SNR trace static s
ax_list.append(MyAxes(fig, [0.57, .67, .2, .05])) # SNR trace static s
ax_list.append(MyAxes(fig, [0.57, .6, .2, .05])) # SNR trace static s
ax_list.append(MyAxes(fig, [0.57, .12, .2, .3])) # SNR rates
#for ax in ax_list: fig.add_axes(ax)
# Text
ax = ax_list[0]
my_nest.ResetKernel()
SNR = create_output_population(
nOutput,
if 0:
groups_dic=simulate_network(params_msn_d1, params_msn_d2, params_stn,
synapse_models, sim_time=sim_time, seed=1,
I_e_add={'SNR':300, 'STN':0,'GPE':30}, threads=4,
start_rec=500.,model_params=model_params)
misc.pickle_save(groups_dic, save_result_at)
else:
groups_dic=misc.pickle_load(save_result_at)
#Inspect results
plot_settings.set_mode(pylab, mode='by_fontsize', w = 1100.0, h = 450.0+175.0, fontsize=12)
font_size_text = 8
fig = pylab.figure( facecolor = 'w' )
ax_list = []
ax_list.append( MyAxes(fig, [ .26-0.21, .85-0.05, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .80-.25, .85-0.05, 0.2 + .165+0.05, .2-0.05 +0.02] ) ) #
ax_list.append( MyAxes(fig, [ .26-0.21, .65-0.04, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .80-.25, .65-0.04, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .26-0.21, .45-0.03, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .80-.25, .45-0.03, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .26-0.21, .25-0.02, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .80-.25, .25-0.02, 0.2 + .165+0.05, .2-0.05 +0.02] ) ) #
ax_list.append( MyAxes(fig, [ .26-0.21, .06, 0.2 + .165+0.05, .2 -0.05+0.02] ) ) #
ax_list.append( MyAxes(fig, [ .80-.25, .06, 0.2 + .165+0.05, .2-0.05+0.02 ] ) ) #
ax=ax_list[0]
plot_example_raster(ax, groups_dic['MSN_D1'], '$MSN_{D1}$')
ax=ax_list[1]
plot_example_firing_rate(ax, groups_dic['MSN_D1'], '$MSN_{D1}$',ylim=[0,35])
