def runs(self):
self.setup()
steps, time = self.steps, self.time
with nest.RunManager():
return [(s, t) for _ in range(steps)
for s, t in self.runner(time, nest.Run)]
def runs(self):
with nest.RunManager():
for _ in range(self.steps):
nest.Run(self.time)
nest.Connect(qsyn_voltmeter, qsyn_neuron)
###############################################################################
# This loop runs over the `n_trials` trials and performs a standard protocol
# of a high-rate response, followed by a pause and then a recovery response.
#
# We actually run over ``n_trials + 1`` rounds, since the first trial is for
# equilibration and is not recorded (see voltmeter parameters above).
#
# We use the NEST ``:class:.RunManager`` to improve performance and call ``:func:.Run``
# inside for each part of the simulation.
#
# We print a line of breadcrumbs to indicate progress.
print(f"Simulating {n_trials} times ", end="", flush=True)
with nest.RunManager():
for t in range(n_trials + 1):
pre_neuron.I_e = I_stim
nest.Run(T_on)
pre_neuron.I_e = 0.0
nest.Run(T_off)
if t % 10 == 0:
print(".", end="", flush=True)
print()
###############################################################################
# Simulate one additional time step. This ensures that the
# voltage traces for all trials, including the last, have the full length, so we
# can easily transform them into a matrix below.