- plt.savefig(f"{out}-concentration-bias.png", dpi=1000)
-
- def simulate(self):
- for i in range(steps):
- print(f"{self.__class__.__name__}, gain: {fmt_num(self.gain[i])}, bias: {fmt_num(self.bias[i])}")
- with nengo.Network() as net:
- # Nodes
- time_node = nengo.Node(output=time_function)
- noise_wm_node = nengo.Node(output=noise_bias_function)
- noise_decision_node = nengo.Node(
- output=noise_decision_function)
-
- # Ensembles
- wm = nengo.Ensemble(neurons_wm, 2)
- wm.gain = np.full(wm.n_neurons, self.gain[i])
- wm.bias = np.full(wm.n_neurons, self.bias[i])
- decision = nengo.Ensemble(neurons_decide, 2)
- inputs = nengo.Ensemble(neurons_inputs, 2)
- output = nengo.Ensemble(neurons_decide, 1)
-
- # Connections
- nengo.Connection(time_node, inputs[1], synapse=None)
- nengo.Connection(inputs, wm, synapse=tau_wm,
- function=inputs_function)
- wm_recurrent = nengo.Connection(wm, wm, synapse=tau_wm,
- function=wm_recurrent_function)
- nengo.Connection(noise_wm_node, wm.neurons, synapse=tau_wm,
- transform=np.ones((neurons_wm, 1)) * tau_wm)
- wm_to_decision = nengo.Connection(
- wm[0], decision[0], synapse=tau)
- nengo.Connection(noise_decision_node,
- decision[1], synapse=None)
- nengo.Connection(decision, output, function=decision_function)
-
- # Probes
- # probes_wm = nengo.Probe(wm[0], synapse=0.01, sample_every=dt_sample)
- # probes_spikes = nengo.Probe(wm.neurons, 'spikes',
- # sample_every=dt_sample)
- # probe_output = nengo.Probe(output, synapse=None, same_every=dt_sample)
-
- # Run simulation
- with nengo.Simulator(net, dt=dt, progress_bar=False) as sim:
- sim.run(t_cue + t_delay)