def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(100000) * 2.5
b, a = signal.butter(8, 0.125)
stimulus = signal.filtfilt(b, a, stimulus)
lif_model = lif.lif(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(stimulus)
return time, v, stimulus, spike_times
def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(100000) * 2.5
b, a = signal.butter(8, 0.125)
stimulus = signal.filtfilt(b, a, stimulus)
lif_model = lif.lif(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(stimulus)
return time, v, stimulus, spike_times
def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(80000) * 2.5
b, a = signal.butter(8, 0.125)
stimulus = signal.filtfilt(b, a, stimulus)
s = np.hstack((np.zeros(10000), stimulus, np.zeros(10000)))
lif_model = lif.lif(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(s)
stimulus_onset = 10000*stepsize
stimulus_duration = len(stimulus)*stepsize
return time, v, stimulus, stimulus_onset, stimulus_duration
def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(80000) * 2.5
b, a = signal.butter(8, 0.125)
stimulus = signal.filtfilt(b, a, stimulus)
s = np.hstack((np.zeros(10000), stimulus, np.zeros(10000)))
lif_model = lif.lif(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(s)
stimulus_onset = 10000 * stepsize
stimulus_duration = len(stimulus) * stepsize
return time, v, stimulus, stimulus_onset, stimulus_duration
def fake_neuron(stepsize=0.001, offset=.8, sta_offset=100):
stimulus = np.random.randn(100000) * 2.5
b, a = signal.butter(8, 0.25)
stimulus = signal.filtfilt(b, a, stimulus)
lif_model = lif.lif(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(stimulus)
snippets = np.zeros((len(spike_times), 2 * sta_offset))
for i,t in enumerate(spike_times):
index = round(t/stepsize)
if index < sta_offset:
snip = stimulus[0:index + sta_offset]
snippets[i, -len(snip):] = snip
elif (index + sta_offset) > len(stimulus):
snip = stimulus[index-sta_offset:]
snippets[i, 0:len(snip)] = snip
else:
snippets[i,:] = stimulus[index - sta_offset:index + sta_offset]
return time, v, spike_times, snippets
def fake_neuron():
lif_model = lif.lif()
t, v, spike_times = lif_model.run_const_stim(10000, 0.005)
return t, v, spike_times
def fake_neuron():
lif_model = lif.lif()
t, v, spike_times = lif_model.run_const_stim(10000, 0.005)
return t, v, spike_times
