def get_synapses(name, source, target, tau_I, A, U, tau_rec, tau_facil=None):
"""Construct connections and retrieve synapses
name -- name of synapses
source -- source of connections
target -- target of connections
tau_I -- inactivation time constant
A -- absolute synaptic strength
U -- utilization of synaptic efficacy
tau_rec -- recovery time constant
tau_facil -- facilitation time constant (optional)
"""
synapses_eqs = """
A : volt
U : 1
tau_I : second
tau_rec : second
dx/dt = z/tau_rec : 1 (clock-driven) # recovered
dy/dt = -y/tau_I : 1 (clock-driven) # active
z = 1 - x - y : 1 # inactive
I_syn_{}_post = A*y : volt (summed)
""".format(name)
if tau_facil:
synapses_eqs += """
du/dt = -u/tau_facil : 1 (clock-driven)
tau_facil : second
"""
synapses_action = """
u += U*(1-u)
y += u*x # important: update y first
x += -u*x
"""
else:
synapses_action = """
y += U*x # important: update y first
x += -U*x
"""
synapses = Synapses(
source,
target,
model=synapses_eqs,
on_pre=synapses_action,
method="exact",
name=name,
)
synapses.connect(p=0.1)
N_syn = len(synapses)
synapses.tau_I = tau_I
A_min = min(0.2 * A, 2 * A)
A_max = max(0.2 * A, 2 * A)
synapses.A = (truncated_normal(
A / mV, 0.5 * abs(A / mV), [A_min / mV, A_max / mV], size=N_syn) * mV)
assert not any(synapses.A < A_min)
assert not any(synapses.A > A_max)
U_mean, U_min, U_max = U
synapses.U = truncated_normal(U_mean,
0.5 * U_mean, [U_min, U_max],
size=N_syn)
assert not any(synapses.U <= U_min)
assert not any(synapses.U > U_max)
tau_min = 5
synapses.tau_rec = (truncated_normal(
tau_rec / ms, 0.5 * tau_rec / ms, [tau_min, np.inf], size=N_syn) * ms)
assert not any(synapses.tau_rec / ms <= tau_min)
if tau_facil:
synapses.tau_facil = (
truncated_normal(tau_facil / ms,
0.5 * tau_facil / ms, [tau_min, np.inf],
size=N_syn) * ms)
assert not any(synapses.tau_facil / ms <= tau_min)
# start fully recovered
synapses.x = 1
return synapses
D=0.144 * second,
F=0.06 * second,
delay=0.8 * ms,
)
# place holder for stimulus
stimulus = SpikeGeneratorGroup(1, [], [] * ms, name="stimulus")
spike_monitor_stimulus = SpikeMonitor(stimulus)
static_synapses_exc = Synapses(stimulus,
exc_neurons,
"A : ampere (shared, constant)",
on_pre="I_stimulus += A")
static_synapses_exc.connect(p=1)
static_synapses_exc.A = 18 * nA
static_synapses_inh = Synapses(stimulus,
inh_neurons,
"A : ampere (shared, constant)",
on_pre="I_stimulus += A")
static_synapses_inh.connect(p=1)
static_synapses_inh.A = 9 * nA
spike_monitor_exc = SpikeMonitor(exc_neurons, name="spike_monitor_exc")
spike_monitor_inh = SpikeMonitor(inh_neurons, name="spike_monitor_inh")
defaultclock.dt = DT
net = Network([
neurons,