def run_isi_simulation():
N = 150
network_params = default_params
trial_duration = 2 * second
var = 10
x1 = 50
x2 = 100
isi_times = range(25, 750, 25)
stim_dur = 100 * ms
adaptation = np.zeros(len(isi_times))
for i, isi in enumerate(isi_times):
print('Testing isi=%dms' % isi)
stim1_start_time = 1 * second
stim1_end_time = stim1_start_time + stim_dur
stim2_start_time = stim1_end_time + isi * ms
stim2_end_time = stim2_start_time + stim_dur
same_pop_monitor, same_voxel_monitor = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [x1, x1],
[var, var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
diff_pop_monitor, diff_voxel_monitor = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [x1, x2],
[var, var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
same_y_max = np.max(same_voxel_monitor['y'][0])
diff_y_max = np.max(diff_voxel_monitor['y'][0])
adaptation[i] = (diff_y_max - same_y_max) / diff_y_max * 100.0
data_dir = '../../data/adaptation/isi'
fig = plt.figure()
plt.plot(isi_times, adaptation)
plt.xlabel('ISI (ms)')
plt.ylabel('Adaptation')
fname = 'adaptation.isi.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
def run_isi_simulation():
N=150
network_params=default_params
trial_duration=2*second
var=10
x1=50
x2=100
isi_times=range(25,750,25)
stim_dur=100*ms
adaptation=np.zeros(len(isi_times))
for i,isi in enumerate(isi_times):
print('Testing isi=%dms' % isi)
stim1_start_time=1*second
stim1_end_time=stim1_start_time+stim_dur
stim2_start_time=stim1_end_time+isi*ms
stim2_end_time=stim2_start_time+stim_dur
same_pop_monitor,same_voxel_monitor=run_pop_code(ProbabilisticPopulationCode, N, network_params, [x1,x1], [var,var],
[stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],trial_duration)
diff_pop_monitor,diff_voxel_monitor=run_pop_code(ProbabilisticPopulationCode, N, network_params, [x1,x2], [var,var],
[stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],trial_duration)
same_y_max=np.max(same_voxel_monitor['y'][0])
diff_y_max=np.max(diff_voxel_monitor['y'][0])
adaptation[i]=(diff_y_max-same_y_max)/diff_y_max*100.0
data_dir='../../data/adaptation/isi'
fig=plt.figure()
plt.plot(isi_times,adaptation)
plt.xlabel('ISI (ms)')
plt.ylabel('Adaptation')
fname='adaptation.isi.%s'
save_to_png(fig, os.path.join(data_dir,fname % 'png'))
save_to_eps(fig, os.path.join(data_dir,fname % 'eps'))
plt.close(fig)
def demo(N, network_params, trial_duration, x1, x2, low_var, high_var, isi, stim_dur):
stim1_start_time=1*second
stim1_end_time=stim1_start_time+stim_dur
stim2_start_time=stim1_end_time+isi
stim2_end_time=stim2_start_time+stim_dur
low_var_prob_pop_monitor,low_var_prob_voxel_monitor=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[x1,x2],[low_var,low_var], [stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],trial_duration)
high_var_prob_pop_monitor,high_var_prob_voxel_monitor=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[x1,x2],[high_var,high_var], [stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],
trial_duration)
low_var_samp_pop_monitor,low_var_samp_voxel_monitor=run_pop_code(SamplingPopulationCode, N, network_params, [x1,x2],
[low_var,low_var], [stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],trial_duration)
high_var_samp_pop_monitor,high_var_samp_voxel_monitor=run_pop_code(SamplingPopulationCode, N, network_params, [x1,x2],
[high_var,high_var], [stim1_start_time,stim2_start_time], [stim1_end_time,stim2_end_time],trial_duration)
data_dir='../../data/adaptation/demo'
fig=plt.figure()
plt.subplot(411)
plt.title('Probabilistic population, low variance - rate')
plt.imshow(low_var_prob_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,.2)
plt.subplot(412)
plt.title('Probabilistic population, high variance - rate')
plt.imshow(high_var_prob_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,.2)
plt.subplot(413)
plt.title('Sampling population, low variance - rate')
plt.imshow(low_var_samp_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,.2)
plt.subplot(414)
plt.title('Sampling population, high variance - rate')
plt.imshow(high_var_samp_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,.2)
fname='firing_rate.%s'
save_to_png(fig, os.path.join(data_dir,fname % 'png'))
save_to_eps(fig, os.path.join(data_dir,fname % 'eps'))
plt.close(fig)
fig=plt.figure()
plt.subplot(411)
plt.title('Probabilistic population, low variance - efficacy')
plt.imshow(low_var_prob_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
plt.subplot(412)
plt.title('Probabilistic population, high variance - efficacy')
plt.imshow(high_var_prob_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
plt.subplot(413)
plt.title('Sampling population, low variance - efficacy')
plt.imshow(low_var_samp_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
plt.subplot(414)
plt.title('Sampling population, high variance - efficacy')
plt.imshow(high_var_samp_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
fname='efficacy.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
fig=plt.figure()
plt.title('BOLD')
plt.plot(low_var_prob_voxel_monitor['y'][0],label='prob,low var')
plt.plot(high_var_prob_voxel_monitor['y'][0],label='prob,high var')
plt.plot(low_var_samp_voxel_monitor['y'][0],label='samp,low var')
plt.plot(high_var_samp_voxel_monitor['y'][0],label='samp,high var')
plt.legend(loc='best')
fname='bold.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
stim1_mid_time=stim1_start_time+(stim1_end_time-stim1_start_time)/2
idx1=int(stim1_mid_time/defaultclock.dt)
stim2_mid_time=stim2_start_time+(stim2_end_time-stim2_start_time)/2
idx2=int(stim2_mid_time/defaultclock.dt)
fig=plt.figure()
plt.title('Probabilistic Population snapshot')
plt.plot(low_var_prob_pop_monitor['r'][:,idx1],'r', label='low var, stim 1')
plt.plot(low_var_prob_pop_monitor['r'][:,idx2],'r--', label='low var stim 2')
plt.plot(high_var_prob_pop_monitor['r'][:,idx1],'b', label='high var, stim 1')
plt.plot(high_var_prob_pop_monitor['r'][:,idx2],'b--', label='high var stim 2')
plt.legend(loc='best')
fname='prob_pop.firing_rate.snapshot.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
fig=plt.figure()
plt.title('Sampling Population snapshot')
plt.plot(low_var_samp_pop_monitor['r'][:,idx1],'r', label='low var, stim 1')
plt.plot(low_var_samp_pop_monitor['r'][:,idx2],'r--', label='low var, stim 2')
plt.plot(high_var_samp_pop_monitor['r'][:,idx1],'b', label='high var, stim 1')
plt.plot(high_var_samp_pop_monitor['r'][:,idx2],'b--', label='high var, stim 2')
plt.legend(loc='best')
fname='samp_pop.firing_rate.snapshot.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
def test_simulation(design):
N=150
network_params=default_params
sim_params=rapid_design_params
network_params.tau_ar=100*ms
if design=='long':
network_params.tau_a=5*second
sim_params=long_design_params
var=10
x1=50
x2=100
stim1_start_time=1*second
stim1_end_time=stim1_start_time+sim_params.stim_dur
stim2_start_time=stim1_end_time+sim_params.isi
stim2_end_time=stim2_start_time+sim_params.stim_dur
same_pop_monitor,same_voxel_monitor,same_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[Stimulus(x1,var,stim1_start_time,stim1_end_time),
Stimulus(x1,var,stim2_start_time,stim2_end_time)],
sim_params.trial_duration)
diff_pop_monitor,diff_voxel_monitor,diff_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[Stimulus(x1,var,stim1_start_time,stim1_end_time),
Stimulus(x2,var,stim2_start_time,stim2_end_time)],
sim_params.trial_duration)
# single_pop_monitor,single_voxel_monitor,single_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
# [Stimulus(x1,var,stim1_start_time,stim1_end_time)],
# sim_params.trial_duration)
data_dir='../../data/adaptation/adaptation_test'
fig=plt.figure()
#plt.subplot(311)
plt.subplot(211)
plt.title('Same')
plt.imshow(same_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
#plt.subplot(312)
plt.subplot(212)
plt.title('Different')
plt.imshow(diff_pop_monitor['r'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
# plt.subplot(313)
# plt.title('Single')
# plt.imshow(single_pop_monitor['r'][:],aspect='auto')
# plt.xlabel('time')
# plt.ylabel('neuron')
# plt.colorbar()
fname='%s.firing_rate' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
fig=plt.figure()
#plt.subplot(311)
plt.subplot(211)
plt.title('Same')
plt.imshow(same_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
#plt.subplot(312)
plt.subplot(212)
plt.title('Different')
plt.imshow(diff_pop_monitor['e'][:],aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0,1)
# plt.subplot(313)
# plt.title('Single')
# plt.imshow(single_pop_monitor['e'][:],aspect='auto')
# plt.xlabel('time')
# plt.ylabel('neuron')
# plt.colorbar()
# plt.clim(0,1)
fname='%s.efficacy' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
fig=plt.figure()
plt.title('BOLD')
plt.plot(same_voxel_monitor['y'][0],label='same')
plt.plot(diff_voxel_monitor['y'][0],label='different')
#plt.plot(single_voxel_monitor['y'][0],label='single')
plt.legend(loc='best')
fname='%s.bold' % design
save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
# fig=plt.figure()
# plt.plot(same_pop_monitor['total_e'][0],label='same')
# plt.plot(diff_pop_monitor['total_e'][0],label='different')
# #plt.plot(single_pop_monitor['total_e'][0],label='single')
# plt.legend(loc='best')
# fname='%s.total_e' % design
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
#
# fig=plt.figure()
# plt.plot(same_pop_monitor['total_r'][0],label='same')
# plt.plot(diff_pop_monitor['total_r'][0],label='different')
# #plt.plot(single_pop_monitor['total_r'][0],label='single')
# plt.legend(loc='best')
# fname='%s.total_r' % design
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
fig=plt.figure()
plt.plot(same_voxel_monitor['G_total'][0][0:20000],label='same')
plt.plot(diff_voxel_monitor['G_total'][0][0:20000],label='different')
#plt.plot(single_voxel_monitor['G_total'][0][0:100000],label='single')
plt.legend(loc='best')
fname='%s.g_total' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
def adaptation_simulation(design, baseline, pop_class, N, network_params, sim_params, stim1_mean, stim2_mean, stim1_var,
stim2_var, data_dir, edesc):
# Compute stimulus start and end times
stim1_start_time=1*second
stim1_end_time=stim1_start_time+sim_params.stim_dur
stim2_start_time=stim1_end_time+sim_params.isi
stim2_end_time=stim2_start_time+sim_params.stim_dur
pop_monitor,voxel_monitor,y_max=run_pop_code(pop_class, N, network_params,
[Stimulus(stim1_mean, stim1_var, stim1_start_time, stim1_end_time),
Stimulus(stim2_mean, stim2_var, stim2_start_time, stim2_end_time)],
sim_params.trial_duration)
if baseline=='repeated':
baseline_pop_monitor,baseline_voxel_monitor,baseline_y_max=run_pop_code(pop_class, N, network_params,
[Stimulus(stim1_mean,stim1_var,stim1_start_time,stim1_end_time),
Stimulus(stim1_mean,stim1_var,stim2_start_time,stim2_end_time)],
sim_params.trial_duration)
adaptation=(y_max-baseline_y_max)/baseline_y_max
elif baseline=='single':
baseline_pop_monitor,baseline_voxel_monitor,baseline_y_max=run_pop_code(pop_class, N, network_params,
[Stimulus(stim2_mean, stim2_var, stim1_start_time, stim1_end_time)],
rapid_design_params.trial_duration)
adaptation=(y_max-baseline_y_max)/baseline_y_max
fig=plt.figure()
plt.plot(voxel_monitor['y'][0], 'b', label='test')
plt.plot(baseline_voxel_monitor['y'][0], 'r', label='baseline')
plt.legend(loc='best')
fname='%s.baseline-%s.%s.%s.bold' % (design,baseline,edesc,pop_class.__name__)
save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
fig=plt.figure()
plt.subplot(211)
plt.title('test')
plt.imshow(pop_monitor['e'][:],aspect='auto')
plt.clim(0,1)
plt.colorbar()
plt.subplot(212)
plt.title('baseline')
plt.imshow(baseline_pop_monitor['e'][:],aspect='auto')
plt.clim(0,1)
plt.colorbar()
fname='%s.baseline-%s.%s.%s.e' % (design,baseline,edesc,pop_class.__name__)
save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
# fig=plt.figure()
# plt.plot(pop_monitor['total_e'][0],'b',label='test')
# plt.plot(baseline_pop_monitor['total_e'][0],'r',label='baseline')
# plt.legend(loc='best')
# fname='%s.baseline-%s.%s.%s.total_e' % (design,baseline,edesc,pop_class.__name__)
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
#
# fig=plt.figure()
# plt.plot(pop_monitor['total_r'][0],'b',label='test')
# plt.plot(baseline_pop_monitor['total_r'][0],'r',label='baseline')
# plt.legend(loc='best')
# fname='%s.baseline-%s.%s.%s.total_r' % (design,baseline,edesc,pop_class.__name__)
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
fig=plt.figure()
plt.plot(voxel_monitor['G_total'][0][0:100000],'b',label='test')
plt.plot(baseline_voxel_monitor['G_total'][0][0:100000],'r',label='baseline')
plt.legend(loc='best')
fname='%s.baseline-%s.%s.%s.g_total' % (design,baseline,edesc,pop_class.__name__)
save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
return adaptation
def run_repeated_test():
N=150
# Set trial duration, inter-stimulus-interval, and stimulus duration depending on design
network_params=default_params
sim_params=rapid_design_params
# Compute stimulus start and end times
stim1_start_time=1*second
stim1_end_time=stim1_start_time+sim_params.stim_dur
stim2_start_time=stim1_end_time+sim_params.isi
stim2_end_time=stim2_start_time+sim_params.stim_dur
x_delta_iter=5
# If baseline is single stimulus - need to test x_delta=0
x_delta_range=np.array(range(0,int(N/3),x_delta_iter))
# High and low variance examples
low_var=5
x=int(N/3)
prob_combined_y_max=np.zeros(len(x_delta_range))
prob_repeated_y_max=np.zeros(len(x_delta_range))
samp_combined_y_max=np.zeros(len(x_delta_range))
samp_repeated_y_max=np.zeros(len(x_delta_range))
for i,x_delta in enumerate(x_delta_range):
print('x_delta=%d' % x_delta)
pop_monitor,voxel_monitor,prob_repeated_y_max[i]=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[Stimulus(x,low_var,stim1_start_time,stim1_end_time),
Stimulus(x+x_delta,low_var,stim2_start_time,stim2_end_time)],
sim_params.trial_duration)
pop_monitor,voxel_monitor,prob_first_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[Stimulus(x, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
pop_monitor,voxel_monitor,prob_second_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
[Stimulus(x+x_delta, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
prob_combined_y_max[i]=prob_first_y_max+prob_second_y_max
pop_monitor,voxel_monitor,samp_repeated_y_max[i]=run_pop_code(SamplingPopulationCode, N, network_params,
[Stimulus(x,low_var,stim1_start_time,stim1_end_time),
Stimulus(x+x_delta,low_var,stim2_start_time,stim2_end_time)],
sim_params.trial_duration)
pop_monitor,voxel_monitor,samp_first_y_max=run_pop_code(SamplingPopulationCode, N, network_params,
[Stimulus(x, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
pop_monitor,voxel_monitor,samp_second_y_max=run_pop_code(SamplingPopulationCode, N, network_params,
[Stimulus(x+x_delta, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
samp_combined_y_max[i]=samp_first_y_max+samp_second_y_max
data_dir='../../data/adaptation/repeated_test/'
fig=plt.figure()
plt.plot(x_delta_range,prob_combined_y_max-prob_repeated_y_max,'r',label='prob')
plt.plot(x_delta_range,samp_combined_y_max-samp_repeated_y_max,'b',label='samp')
plt.legend(loc='best')
fname='repeated_test'
save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
plt.close(fig)
def demo(N, network_params, trial_duration, x1, x2, low_var, high_var, isi,
stim_dur):
stim1_start_time = 1 * second
stim1_end_time = stim1_start_time + stim_dur
stim2_start_time = stim1_end_time + isi
stim2_end_time = stim2_start_time + stim_dur
low_var_prob_pop_monitor, low_var_prob_voxel_monitor = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [x1, x2],
[low_var, low_var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
high_var_prob_pop_monitor, high_var_prob_voxel_monitor = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [x1, x2],
[high_var, high_var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
low_var_samp_pop_monitor, low_var_samp_voxel_monitor = run_pop_code(
SamplingPopulationCode, N, network_params, [x1, x2],
[low_var, low_var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
high_var_samp_pop_monitor, high_var_samp_voxel_monitor = run_pop_code(
SamplingPopulationCode, N, network_params, [x1, x2],
[high_var, high_var], [stim1_start_time, stim2_start_time],
[stim1_end_time, stim2_end_time], trial_duration)
data_dir = '../../data/adaptation/demo'
fig = plt.figure()
plt.subplot(411)
plt.title('Probabilistic population, low variance - rate')
plt.imshow(low_var_prob_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, .2)
plt.subplot(412)
plt.title('Probabilistic population, high variance - rate')
plt.imshow(high_var_prob_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, .2)
plt.subplot(413)
plt.title('Sampling population, low variance - rate')
plt.imshow(low_var_samp_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, .2)
plt.subplot(414)
plt.title('Sampling population, high variance - rate')
plt.imshow(high_var_samp_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, .2)
fname = 'firing_rate.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
fig = plt.figure()
plt.subplot(411)
plt.title('Probabilistic population, low variance - efficacy')
plt.imshow(low_var_prob_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
plt.subplot(412)
plt.title('Probabilistic population, high variance - efficacy')
plt.imshow(high_var_prob_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
plt.subplot(413)
plt.title('Sampling population, low variance - efficacy')
plt.imshow(low_var_samp_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
plt.subplot(414)
plt.title('Sampling population, high variance - efficacy')
plt.imshow(high_var_samp_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
fname = 'efficacy.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
fig = plt.figure()
plt.title('BOLD')
plt.plot(low_var_prob_voxel_monitor['y'][0], label='prob,low var')
plt.plot(high_var_prob_voxel_monitor['y'][0], label='prob,high var')
plt.plot(low_var_samp_voxel_monitor['y'][0], label='samp,low var')
plt.plot(high_var_samp_voxel_monitor['y'][0], label='samp,high var')
plt.legend(loc='best')
fname = 'bold.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
stim1_mid_time = stim1_start_time + (stim1_end_time - stim1_start_time) / 2
idx1 = int(stim1_mid_time / defaultclock.dt)
stim2_mid_time = stim2_start_time + (stim2_end_time - stim2_start_time) / 2
idx2 = int(stim2_mid_time / defaultclock.dt)
fig = plt.figure()
plt.title('Probabilistic Population snapshot')
plt.plot(low_var_prob_pop_monitor['r'][:, idx1],
'r',
label='low var, stim 1')
plt.plot(low_var_prob_pop_monitor['r'][:, idx2],
'r--',
label='low var stim 2')
plt.plot(high_var_prob_pop_monitor['r'][:, idx1],
'b',
label='high var, stim 1')
plt.plot(high_var_prob_pop_monitor['r'][:, idx2],
'b--',
label='high var stim 2')
plt.legend(loc='best')
fname = 'prob_pop.firing_rate.snapshot.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
fig = plt.figure()
plt.title('Sampling Population snapshot')
plt.plot(low_var_samp_pop_monitor['r'][:, idx1],
'r',
label='low var, stim 1')
plt.plot(low_var_samp_pop_monitor['r'][:, idx2],
'r--',
label='low var, stim 2')
plt.plot(high_var_samp_pop_monitor['r'][:, idx1],
'b',
label='high var, stim 1')
plt.plot(high_var_samp_pop_monitor['r'][:, idx2],
'b--',
label='high var, stim 2')
plt.legend(loc='best')
fname = 'samp_pop.firing_rate.snapshot.%s'
save_to_png(fig, os.path.join(data_dir, fname % 'png'))
save_to_eps(fig, os.path.join(data_dir, fname % 'eps'))
plt.close(fig)
def test_simulation(design):
N = 150
network_params = default_params
sim_params = rapid_design_params
network_params.tau_ar = 100 * ms
if design == 'long':
network_params.tau_a = 5 * second
sim_params = long_design_params
var = 10
x1 = 50
x2 = 100
stim1_start_time = 1 * second
stim1_end_time = stim1_start_time + sim_params.stim_dur
stim2_start_time = stim1_end_time + sim_params.isi
stim2_end_time = stim2_start_time + sim_params.stim_dur
same_pop_monitor, same_voxel_monitor, same_y_max = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [
Stimulus(x1, var, stim1_start_time, stim1_end_time),
Stimulus(x1, var, stim2_start_time, stim2_end_time)
], sim_params.trial_duration)
diff_pop_monitor, diff_voxel_monitor, diff_y_max = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [
Stimulus(x1, var, stim1_start_time, stim1_end_time),
Stimulus(x2, var, stim2_start_time, stim2_end_time)
], sim_params.trial_duration)
# single_pop_monitor,single_voxel_monitor,single_y_max=run_pop_code(ProbabilisticPopulationCode, N, network_params,
# [Stimulus(x1,var,stim1_start_time,stim1_end_time)],
# sim_params.trial_duration)
data_dir = '../../data/adaptation/adaptation_test'
fig = plt.figure()
#plt.subplot(311)
plt.subplot(211)
plt.title('Same')
plt.imshow(same_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
#plt.subplot(312)
plt.subplot(212)
plt.title('Different')
plt.imshow(diff_pop_monitor['r'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
# plt.subplot(313)
# plt.title('Single')
# plt.imshow(single_pop_monitor['r'][:],aspect='auto')
# plt.xlabel('time')
# plt.ylabel('neuron')
# plt.colorbar()
fname = '%s.firing_rate' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
fig = plt.figure()
#plt.subplot(311)
plt.subplot(211)
plt.title('Same')
plt.imshow(same_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
#plt.subplot(312)
plt.subplot(212)
plt.title('Different')
plt.imshow(diff_pop_monitor['e'][:], aspect='auto')
plt.xlabel('time')
plt.ylabel('neuron')
plt.colorbar()
plt.clim(0, 1)
# plt.subplot(313)
# plt.title('Single')
# plt.imshow(single_pop_monitor['e'][:],aspect='auto')
# plt.xlabel('time')
# plt.ylabel('neuron')
# plt.colorbar()
# plt.clim(0,1)
fname = '%s.efficacy' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
fig = plt.figure()
plt.title('BOLD')
plt.plot(same_voxel_monitor['y'][0], label='same')
plt.plot(diff_voxel_monitor['y'][0], label='different')
#plt.plot(single_voxel_monitor['y'][0],label='single')
plt.legend(loc='best')
fname = '%s.bold' % design
save_to_png(fig, os.path.join(data_dir, '%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
# fig=plt.figure()
# plt.plot(same_pop_monitor['total_e'][0],label='same')
# plt.plot(diff_pop_monitor['total_e'][0],label='different')
# #plt.plot(single_pop_monitor['total_e'][0],label='single')
# plt.legend(loc='best')
# fname='%s.total_e' % design
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
#
# fig=plt.figure()
# plt.plot(same_pop_monitor['total_r'][0],label='same')
# plt.plot(diff_pop_monitor['total_r'][0],label='different')
# #plt.plot(single_pop_monitor['total_r'][0],label='single')
# plt.legend(loc='best')
# fname='%s.total_r' % design
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
fig = plt.figure()
plt.plot(same_voxel_monitor['G_total'][0][0:20000], label='same')
plt.plot(diff_voxel_monitor['G_total'][0][0:20000], label='different')
#plt.plot(single_voxel_monitor['G_total'][0][0:100000],label='single')
plt.legend(loc='best')
fname = '%s.g_total' % design
#save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
def adaptation_simulation(design, baseline, pop_class, N, network_params,
sim_params, stim1_mean, stim2_mean, stim1_var,
stim2_var, data_dir, edesc):
# Compute stimulus start and end times
stim1_start_time = 1 * second
stim1_end_time = stim1_start_time + sim_params.stim_dur
stim2_start_time = stim1_end_time + sim_params.isi
stim2_end_time = stim2_start_time + sim_params.stim_dur
pop_monitor, voxel_monitor, y_max = run_pop_code(
pop_class, N, network_params, [
Stimulus(stim1_mean, stim1_var, stim1_start_time, stim1_end_time),
Stimulus(stim2_mean, stim2_var, stim2_start_time, stim2_end_time)
], sim_params.trial_duration)
if baseline == 'repeated':
baseline_pop_monitor, baseline_voxel_monitor, baseline_y_max = run_pop_code(
pop_class, N, network_params, [
Stimulus(stim1_mean, stim1_var, stim1_start_time,
stim1_end_time),
Stimulus(stim1_mean, stim1_var, stim2_start_time,
stim2_end_time)
], sim_params.trial_duration)
adaptation = (y_max - baseline_y_max) / baseline_y_max
elif baseline == 'single':
baseline_pop_monitor, baseline_voxel_monitor, baseline_y_max = run_pop_code(
pop_class, N, network_params, [
Stimulus(stim2_mean, stim2_var, stim1_start_time,
stim1_end_time)
], rapid_design_params.trial_duration)
adaptation = (y_max - baseline_y_max) / baseline_y_max
fig = plt.figure()
plt.plot(voxel_monitor['y'][0], 'b', label='test')
plt.plot(baseline_voxel_monitor['y'][0], 'r', label='baseline')
plt.legend(loc='best')
fname = '%s.baseline-%s.%s.%s.bold' % (design, baseline, edesc,
pop_class.__name__)
save_to_png(fig, os.path.join(data_dir, '%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
fig = plt.figure()
plt.subplot(211)
plt.title('test')
plt.imshow(pop_monitor['e'][:], aspect='auto')
plt.clim(0, 1)
plt.colorbar()
plt.subplot(212)
plt.title('baseline')
plt.imshow(baseline_pop_monitor['e'][:], aspect='auto')
plt.clim(0, 1)
plt.colorbar()
fname = '%s.baseline-%s.%s.%s.e' % (design, baseline, edesc,
pop_class.__name__)
save_to_png(fig, os.path.join(data_dir, '%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
# fig=plt.figure()
# plt.plot(pop_monitor['total_e'][0],'b',label='test')
# plt.plot(baseline_pop_monitor['total_e'][0],'r',label='baseline')
# plt.legend(loc='best')
# fname='%s.baseline-%s.%s.%s.total_e' % (design,baseline,edesc,pop_class.__name__)
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
#
# fig=plt.figure()
# plt.plot(pop_monitor['total_r'][0],'b',label='test')
# plt.plot(baseline_pop_monitor['total_r'][0],'r',label='baseline')
# plt.legend(loc='best')
# fname='%s.baseline-%s.%s.%s.total_r' % (design,baseline,edesc,pop_class.__name__)
# save_to_png(fig, os.path.join(data_dir,'%s.png' % fname))
# save_to_eps(fig, os.path.join(data_dir,'%s.eps' % fname))
# plt.close(fig)
fig = plt.figure()
plt.plot(voxel_monitor['G_total'][0][0:100000], 'b', label='test')
plt.plot(baseline_voxel_monitor['G_total'][0][0:100000],
'r',
label='baseline')
plt.legend(loc='best')
fname = '%s.baseline-%s.%s.%s.g_total' % (design, baseline, edesc,
pop_class.__name__)
save_to_png(fig, os.path.join(data_dir, '%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)
return adaptation
def run_repeated_test():
N = 150
# Set trial duration, inter-stimulus-interval, and stimulus duration depending on design
network_params = default_params
sim_params = rapid_design_params
# Compute stimulus start and end times
stim1_start_time = 1 * second
stim1_end_time = stim1_start_time + sim_params.stim_dur
stim2_start_time = stim1_end_time + sim_params.isi
stim2_end_time = stim2_start_time + sim_params.stim_dur
x_delta_iter = 5
# If baseline is single stimulus - need to test x_delta=0
x_delta_range = np.array(range(0, int(N / 3), x_delta_iter))
# High and low variance examples
low_var = 5
x = int(N / 3)
prob_combined_y_max = np.zeros(len(x_delta_range))
prob_repeated_y_max = np.zeros(len(x_delta_range))
samp_combined_y_max = np.zeros(len(x_delta_range))
samp_repeated_y_max = np.zeros(len(x_delta_range))
for i, x_delta in enumerate(x_delta_range):
print('x_delta=%d' % x_delta)
pop_monitor, voxel_monitor, prob_repeated_y_max[i] = run_pop_code(
ProbabilisticPopulationCode, N, network_params, [
Stimulus(x, low_var, stim1_start_time, stim1_end_time),
Stimulus(x + x_delta, low_var, stim2_start_time,
stim2_end_time)
], sim_params.trial_duration)
pop_monitor, voxel_monitor, prob_first_y_max = run_pop_code(
ProbabilisticPopulationCode, N, network_params,
[Stimulus(x, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
pop_monitor, voxel_monitor, prob_second_y_max = run_pop_code(
ProbabilisticPopulationCode, N, network_params,
[Stimulus(x + x_delta, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
prob_combined_y_max[i] = prob_first_y_max + prob_second_y_max
pop_monitor, voxel_monitor, samp_repeated_y_max[i] = run_pop_code(
SamplingPopulationCode, N, network_params, [
Stimulus(x, low_var, stim1_start_time, stim1_end_time),
Stimulus(x + x_delta, low_var, stim2_start_time,
stim2_end_time)
], sim_params.trial_duration)
pop_monitor, voxel_monitor, samp_first_y_max = run_pop_code(
SamplingPopulationCode, N, network_params,
[Stimulus(x, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
pop_monitor, voxel_monitor, samp_second_y_max = run_pop_code(
SamplingPopulationCode, N, network_params,
[Stimulus(x + x_delta, low_var, stim1_start_time, stim1_end_time)],
sim_params.trial_duration)
samp_combined_y_max[i] = samp_first_y_max + samp_second_y_max
data_dir = '../../data/adaptation/repeated_test/'
fig = plt.figure()
plt.plot(x_delta_range,
prob_combined_y_max - prob_repeated_y_max,
'r',
label='prob')
plt.plot(x_delta_range,
samp_combined_y_max - samp_repeated_y_max,
'b',
label='samp')
plt.legend(loc='best')
fname = 'repeated_test'
save_to_png(fig, os.path.join(data_dir, '%s.png' % fname))
save_to_eps(fig, os.path.join(data_dir, '%s.eps' % fname))
plt.close(fig)