def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(102000) * 2.5
b, a = signal.butter(2, 7.5, fs=1. / stepsize, btype="low")
stimulus = signal.filtfilt(b, a, stimulus)
stimulus = stimulus[1000:-1000]
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, 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 = int(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(offset=1.0)
t, v, spike_times = lif_model.run_const_stim(5000, 0.00025)
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