# 'i_offset': i_offsets,
# }
sim_time = 100.0
timestep = 1.0
sim.setup(timestep=timestep, min_delay=timestep, backend='SingleThreadedCPU')
post = sim.Population(n_neurons, neuron_class(**base_params))
post.record('spikes')
post.record('v_thresh_adapt')
post.record('v')
pre = sim.Population(
n_neurons,
sim.SpikeSourceArray(spike_times=[[10] for _ in range(n_neurons)]),
)
proj = sim.Projection(pre, post, sim.OneToOneConnector(),
sim.StaticSynapse(weight=3.2))
sim.run(sim_time)
data = post.get_data()
sim.end()
vthresh = data.segments[0].filter(name='v_thresh_adapt')
v = data.segments[0].filter(name='v')
np.savez_compressed("genn_spiking_behaviour.npz", spikes=data.segments[0])
for ssa_id in range(n_ssa):
sources[ssa_id] = {}
spike_times = []
for nid in range(n_neurons[ssa_id]):
n_spikes = int(np.random.choice(spikes_per_neuron))
spikes = np.round(np.random.choice(max_t, size=n_spikes,
replace=False)).tolist()
spikes[:] = sorted(spikes)
spike_times.append(sorted(spikes))
sources[ssa_id]['times'] = spike_times
ssa = sim.Population(n_neurons[ssa_id],
sim.SpikeSourceArray(spike_times=spike_times),
label='ssa %d' % ssa_id)
ssa.record(['spikes'])
sources[ssa_id]['pop'] = ssa
sim.run(int(1.5 * max_t))
for ssa_id in range(n_ssa):
data = sources[ssa_id]['pop'].get_data()
sources[ssa_id]['spikes'] = spikes_from_data(data)
sources[ssa_id]['data'] = data
sim.end()
for ssa_id in range(n_ssa):
num_neurons = num_dt
sim.setup(timestep=timestep, min_delay=timestep, backend='SingleThreadedCPU')
pprojs = {}
for delay in delays:
projs = {}
for dt in range(start_dt, start_dt + num_dt, 1): # dt <- (-max_dt, max_dt)
pre_spike_times = [[trigger_t + dt]] # pre = (post + dt)
trigger_spike_times = [[trigger_t]] # post spike time
# print(trigger_t, pre_spike_times[0][0], dt)
# print("post - pre ", trigger_t - pre_spike_times[0][0])
# makes post population spike
trigger = sim.Population(
1, sim.SpikeSourceArray(**{'spike_times': trigger_spike_times}))
post = sim.Population(1, neuron_class(**base_params))
post.record('spikes')
pre = sim.Population(
1, sim.SpikeSourceArray(**{'spike_times': pre_spike_times}))
tr2post = sim.Projection(trigger,
post,
sim.OneToOneConnector(),
synapse_type=sim.StaticSynapse(weight=2.0),
receptor_type='excitatory',
label='trigger connection')
tdep = __stdp__.MyTemporalDependence(**time_dep_vars)