def run_simulation(adjacency_matrix=None,
weight=None,
noise_rate=NOISE_RATE,
noise_weight=NOISE_WEIGHT,
resolution=RESOLUTION,
simtime=SIMTIME_RUN,
save=False, output_path='data/', basename='nest_sim_',
overwrite=False, verbose=True, print_time=False):
if adjacency_matrix is None:
# construct a network according to defaults
adjacency_matrix = fake_network.construct_network()
if weight is None:
# if unspecified, find the weight automatically according to defaults
weight, _ = adjust_weight(adjacency_matrix, noise_weight=noise_weight,
noise_rate=noise_rate, resolution=resolution,
verbose=verbose, print_time=print_time)
# convert into network_object
network_obj = adjacency2netobj(adjacency_matrix)
ncells, ncons, neuronsE, espikes, noise, GIDoffset = create_network(
network_obj, weight, noise_weight, noise_rate, resolution=resolution,
verbose=verbose, print_time=print_time,
)
if verbose:
print 'Simulating %s of activity for %i neurons' % (
s2h(simtime / 1000.), ncells)
startsimulate = time.time()
nest.Simulate(simtime)
endsimulate = time.time()
sim_elapsed = endsimulate - startsimulate
totalspikes = nest.GetStatus(espikes, "n_events")[0]
events = nest.GetStatus(espikes, "events")[0]
# NEST increments the GID whenever a new node is created. we therefore
# subtract the GID offset, so that the output cell indices are correct
# regardless of when the corresponding nodes were created in NEST
cell_indices = events["senders"] - GIDoffset
spike_times = events["times"]
burst_rate = determine_burst_rate(spike_times, simtime, ncells)
if verbose:
print "\n" + "-" * 60
print "Number of neurons: ", ncells
print "Number of spikes recorded: ", totalspikes
print "Avg. spike rate of neurons: %.2f Hz" % (
totalspikes / (ncells * simtime / 1000.))
print "Network burst rate: %.2f Hz" % burst_rate
print "Simulation time: %s" % s2h(sim_elapsed)
print "-" * 60
# resample at 50Hz to make an [ncells, ntimesteps] array of bin counts
resampled = resample_spikes(spike_times, cell_indices,
output_resolution=20, simtime=simtime,
ncells=ncells)
if save:
today = str(datetime.date.today())
fname = basename + today
if os.path.exists(fname) & ~overwrite:
suffix = 0
while os.path.exists(fname):
suffix += 1
fname = '%s%s_%i.npz' % (basename, today, suffix)
fullpath = os.path.join(output_path, fname)
np.savez(fullpath, spike_times=spike_times, cell_indices=cell_indices,
resampled=resampled)
if verbose:
print "Saved output in '%s'" % fullpath
return spike_times, cell_indices, resampled
def adjust_weight(adjacency_matrix=None,
burstrate_target=BURSTRATE_TARGET,
burstrate_tol=BURSTRATE_TOL,
weight=JE_WEIGHT_INIT,
noise_weight=NOISE_WEIGHT,
noise_rate=NOISE_RATE,
resolution=RESOLUTION,
simtime=SIMTIME_ADAPT,
maxiter_adapt=MAXITER_ADAPT,
verbose=True,
print_time=False,
):
if adjacency_matrix is None:
# construct a default network
adjacency_matrix = fake_network.construct_network()
# convert into network_object
network_obj = adjacency2netobj(adjacency_matrix)
adaptParList = []
burst_rate = -1
adaptation_iteration = 1
last_burst_rates = []
last_JEs = []
if verbose:
print "Starting adaptation phase..."
while abs(burst_rate - burstrate_target) > burstrate_tol:
if (len(last_burst_rates) < 2
or last_burst_rates[-1] == last_burst_rates[-2]):
if len(last_burst_rates) > 0:
if verbose:
print 'Auto-burst stage II. - changing weight by 10%'
if burst_rate > burstrate_target:
weight *= 0.9
else:
weight *= 1.1
else:
if verbose:
print 'Auto-burst stage I. - initial run'
else:
if verbose:
print 'Auto-burst stage III. - linear extrapolation'
weight = (((burstrate_target - last_burst_rates[-2])
* (last_JEs[-1] - last_JEs[-2])
/ (last_burst_rates[-1] - last_burst_rates[-2]))
+ last_JEs[-2])
assert weight > 0.
if verbose:
print "adaptation %i, setting weight to %g ..." % (
adaptation_iteration, weight)
print 'Setting up network...'
ncells, ncons, neuronsE, espikes, noise, GIDoffset = create_network(
network_obj, weight, noise_weight, noise_rate,
resolution=resolution, verbose=verbose, print_time=print_time,
)
if verbose:
print 'Simulating...'
nest.Simulate(simtime)
if verbose:
print 'Calculating the burst rate...'
spike_times = nest.GetStatus(espikes, "events")[0]["times"]
burst_rate = determine_burst_rate(spike_times, simtime, ncells)
if verbose:
print "-> the burst rate is %g Hz" % burst_rate
adaptation_iteration += 1
last_burst_rates.append(burst_rate)
last_JEs.append(weight)
assert adaptation_iteration < maxiter_adapt
return weight, burst_rate