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demo.py
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demo.py
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from collections import defaultdict
import plotly.graph_objs as go
def store(state, **kwargs):
for k, v in kwargs.items():
state[k].append(v)
def f(x, dt):
return x * dt
def bin_spikes(spikes, t0, tmax, dt):
n_bins = int((tmax - t0) // dt + 3)
spike_bins = [0 for _ in range(n_bins)]
for spike in spikes:
timestep = (spike - t0) // dt
spike_bins[int(timestep)] += 1
return iter(spike_bins)
def simulate(t0, tmax, dt, spikes):
# Initialize state
state = defaultdict(list)
# Constants
A, U, tau_d, tau_f, tau_s = 1, 0.45, 0.02, 0.45, 0.08
# Initial condition
t, x, u, I = t0, 1, 0, 0
store(state, t=t, x=x, u=u, I=I)
# Calculate timesteps at which spikes occur
spike_bins = bin_spikes(spikes, t0, tmax, dt)
# Solve simulation timestep
while t < tmax:
n_spikes = next(spike_bins)
# (1) Potentiate the synapse
u += -u / tau_f + U * (1 - u) * n_spikes
# (2) Produce current jump
I += -I / tau_s + A * u * x * n_spikes
# (3) Expend resources
x += (1 - x) / tau_d - u * x * n_spikes
# Advance timestep
t += dt
# Store state
store(state, t=t, x=x, u=u, I=I)
return state
def plot(state):
t = state["t"]
go.Figure([go.Scatter(x=t, y=v, name=k) for k, v in state.items()]).write_html(
"out.html"
)
plot(simulate(0, 100, 0.1, [90, 91, 91, 92, 92.1]))