def test_resonator():
p, s = izk.resonator
ts = np.arange(0, 400, 0.25)
T1 = ts[-1] / 10
T2 = T1 + 20
T3 = 0.7 * ts[-1]
T4 = T3 + 40
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if (
((t > T1) and (t < T1 + 4))
or ((t > T2) and (t < T2 + 4))
or ((t > T3) and (t < T3 + 4))
or ((t > T4) and (t < T4 + 4))
):
input_current = 0.65 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_integrator():
p, s = izk.integrator
ts = np.arange(0, 100, 0.25)
T1 = ts[-1] / 11
T2 = T1 + 5
T3 = 0.7 * ts[-1]
T4 = T3 + 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if (
((t > T1) and (t < T1 + 2))
or ((t > T2) and (t < T2 + 2))
or ((t > T3) and (t < T3 + 2))
or ((t > T4) and (t < T4 + 2))
):
input_current = 9 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def plot_izhikevich(
behavior: IzhikevichSpikingBehavior,
current: float = 1,
time_print: int = 250,
time_current: int = 20,
timestep_print: float = 0.1,
):
"""
Computes and plots a 2D visualisation of the behavior of an Izhikevich neuron model.
By default, the time window is 250ms with a time step of 0.1ms
Example :
>>> import torch
>>> from norse.torch import tonic_spiking
>>> from norse.torch.utils.plot import plot_izhikevich
>>> plot_izhikevich(tonic_spiking)
.. plot::
import torch
from norse.torch import tonic_spiking
from norse.torch.utils.plot import plot_izhikevich
plot_izhikevich(tonic_spiking)
Arguments :
behavior (IzhikevichSpikingBehavior) : behavior of an Izhikevich neuron
current (float) : strengh of the input current, defaults to 1
time_print (float) : size of the time window for simulation (in ms)
time_current (float) : time at which the input current goes from 0 to current (in ms)
timestep_print (float) : timestep of the simulation (in ms)
"""
p, s = behavior
T1 = time_current
vs = []
us = []
cs = []
time = []
for t in np.arange(0, time_print, timestep_print):
vs.append(s.v.item())
us.append(s.u.item())
time.append(t * timestep_print)
if t > T1:
input_current = current * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
_, s = izhikevich_step(input_current, s, p)
cs.append(input_current)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_ylabel("Membrane potential (mV)")
ax1.set_xlabel("Time (ms)")
ax1.plot(time, vs)
ax1.plot(time, cs)
def test_phasic_bursting():
p, s = izk.phasic_bursting
T1 = 20
vs = []
us = []
cs = []
for t in np.arange(0, 200, 0.25):
vs.append(s.v)
us.append(s.u)
if t > T1:
input_current = 0.6 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
_, s = izhikevich_step(input_current, s, p)
cs.append(input_current)
return vs, cs
def test_inhibition_induced_spiking():
p, s = izk.inhibition_induced_spiking
ts = np.arange(0, 350, 0.5)
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if t < 50 or t > 250:
input_current = 80.0 * torch.ones(1)
else:
input_current = 75.0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_tonic_spiking():
p, s = izk.tonic_spiking
ts = np.arange(0, 100, 0.25)
T1 = ts[-1] / 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if t > T1:
input_current = 14 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
_, s = izhikevich_step(input_current, s, p)
cs.append(input_current)
return vs, cs
def test_mixed_mode():
p, s = izk.mixed_mode
ts = np.arange(0, 160, 0.25)
T1 = ts[-1] / 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if t > T1:
input_current = 10 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
_, s = izhikevich_step(input_current, s, p)
cs.append(input_current)
return vs, cs
def test_subthreshold_oscillation():
p, s = izk.subthreshold_oscillation
ts = np.arange(0, 200, 0.25)
T1 = ts[-1] / 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if (t > T1) and (t < T1 + 5):
input_current = 2.0 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_spike_latency():
p, s = izk.spike_latency
ts = np.arange(0, 100, 0.2)
T1 = ts[-1] / 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if (t > T1) and (t < T1 + 3):
input_current = 7.4 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_class_2_exc():
p, s = izk.class_2_exc
ts = np.arange(0, 300, 0.25)
T1 = 30
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if t > T1:
input_current = (-0.5 + (0.015 * (t - T1))) * torch.ones(1)
else:
input_current = -0.5 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs
def test_spike_frequency_adaptation():
p, s = izk.spike_frequency_adaptation
ts = np.arange(0, 85, 0.25)
T1 = ts[-1] / 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if t > T1:
input_current = 30 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
_, s = izhikevich_step(input_current, s, p)
cs.append(input_current)
return vs, cs
def test_dap():
p, s = izk.dap
ts = np.arange(0, 50, 0.1)
T1 = 10
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if abs(t - T1) < 1:
input_current = 20.0 * torch.ones(1)
else:
input_current = 0.0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_rebound_burst():
p, s = izk.rebound_burst
ts = np.arange(0, 200, 0.2)
T1 = 20
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if (t > T1) and (t < T1 + 5):
input_current = -15 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_bistability():
p, s = izk.bistability
ts = np.arange(0, 300, 0.25)
T1 = ts[-1] / 8
T2 = 216
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if ((t > T1) and (t < T1 + 5)) or ((t > T2) and (t < T2 + 5)):
input_current = 1.24 * torch.ones(1)
else:
input_current = 0.24 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
def test_threshhold_variability():
p, s = izk.threshhold_variability
ts = np.arange(0, 100, 0.25)
T1 = 10
T2 = 80
vs = []
us = []
cs = []
for t in ts:
vs.append(s.v)
us.append(s.u)
if ((t > T1) and (t < T1 + 5)) or ((t > T2) and (t < T2 + 5)):
input_current = 1 * torch.ones(1)
elif (t > T2 - 10) and (t < T2 - 10 + 5):
input_current = -6 * torch.ones(1)
else:
input_current = 0 * torch.ones(1)
cs.append(input_current)
_, s = izhikevich_step(input_current, s, p)
return vs, cs
