+
+
+class Simulation():
+ def __init__(self):
+ self.a1 = Alpha1()
+ self.a1.plot()
+ self.a2 = Alpha2()
+ self.a2.plot()
+
+ self.num_spikes = np.zeros(len(steps))
+ self.num_correct = np.zeros(len(steps))
+ self.out = np.zeros(n_trials)
+ self.trial = 0
+
+ # correctly perceived (not necessarily remembered) cues
+ self.perceived = np.ones(n_trials)
+ rng = np.random.RandomState(seed=seed)
+ # whether the cues is on the left or right
+ self.cues = 2 * rng.randint(2, size=n_trials)-1
+ for n in range(len(self.perceived)):
+ if rng.rand() < misperceive:
+ self.perceived[n] = 0
+
+ def plot(self):
+ title = "Norepinephrine Concentration vs Spiking Rate"
+ logging.info("Plotting " + title)
+ plt.figure()
+ plt.plot(steps, self.num_spikes)
+
+ plt.xlabel("Norepinephrine concentration (nM)")
+ plt.ylabel("Spiking rate (spikes/time step)")
+ plt.title(title)
+
+ plt.draw()
+ plt.savefig("./out/concentration-spiking.png")
+
+ ########################################################################
+
+ title = "Norepinephrine Concentration vs Accuracy"
+ logging.info("Plotting " + title)
+ plt.figure()
+ correct_df = pd.DataFrame(np.clip(self.num_correct, 0.5, 1.0)).rolling(20).mean()
+ plt.plot(steps, correct_df)
+
+ plt.xlabel("Norepinephrine concentration (nM)")
+ plt.ylabel("Accuracy")
+ plt.title(title)
+
+ plt.draw()
+ plt.savefig("./out/concentration-correct.png")
+
+ def cue_function(self, t):
+ if t < t_cue and self.perceived[self.trial] != 0:
+ return cue_scale * self.cues[self.trial]
+ else:
+ return 0
+
+ def run(self):
+ with nengo.Network() as net:
+ # Nodes
+ cue_node = nengo.Node(output=self.cue_function)
+ 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)
+ decision = nengo.Ensemble(neurons_decide, 2)
+ inputs = nengo.Ensemble(neurons_inputs, 2)
+ output = nengo.Ensemble(neurons_decide, 1)
+
+ # Connections
+ nengo.Connection(cue_node, inputs[0], synapse=None)
+ 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)
+ 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
+ wm_probe = nengo.Probe(wm[0], synapse=0.01, sample_every=probe_dt)
+ spikes_probe = nengo.Probe(wm.neurons, sample_every=probe_dt)
+ output_probe = nengo.Probe(
+ output, synapse=None, sample_every=probe_dt)
+
+ # Run simulation
+ for i, _ in tqdm(enumerate(steps), total=len(steps), unit="step"):
+ sim = nengo.Simulator(net, dt=dt, progress_bar=False)
+ wm.gain = (self.a1.gains[i] + self.a2.gains[i]) * sim.data[wm].gain
+ wm.bias = (self.a1.biass[i] + self.a2.biass[i]) * sim.data[wm].bias
+ wm_recurrent.solver = MySolver(
+ sim.model.params[wm_recurrent].weights)
+ wm_to_decision.solver = MySolver(
+ sim.model.params[wm_to_decision].weights)
+ sim = nengo.Simulator(net, dt=dt, progress_bar=False)
+ for self.trial in range(n_trials):
+ logging.info(
+ f"Simulating: trial: {self.trial}, gain: {fmt_num(wm.gain)}, bias: {fmt_num(wm.bias)}")
+ sim.run(t_cue + t_delay)
+
+ # Firing rate
+ self.out[self.trial] = np.count_nonzero(
+ sim.data[spikes_probe])
+
+ cue = self.cues[self.trial]
+ # Correctness
+ out = sim.data[output_probe][int(t_cue + t_delay)][0]
+ if (out * cue) > 0: # check if same sign
+ self.num_correct[i] += np.abs(1 / (out - cue))
+
+ self.num_spikes[i] = np.average(self.out)
+
+ with open(f"out/{datetime.now().isoformat()}-spikes.pkl", "wb") as pout:
+ pickle.dump(self, pout)
+
+ self.plot()
+
+def get_correct(cue, output_value):
+ return 1 if (cue > 0.0 and output_value > 0.0) or (cue < 0.0 and output_value < 0.0) else 0
+
+
+class MySolver(nengo.solvers.Solver):
+ def __init__(self, weights):
+ self.weights = False
+ self.my_weights = weights
+ self._paramdict = {}
+
+ def __call__(self, A, Y, rng=None, E=None):
+ return self.my_weights.T, dict()
+