exec(open("conf.py").read())
+
def fmt_num(num, width=18):
"""
Format number to string.
elif value < 0.0:
output = -1.0
return output
- #return 1.0 if x[0] + x[1] > 0.0 else -1.0
+ # return 1.0 if x[0] + x[1] > 0.0 else -1.0
class Alpha():
self.biass = []
for i in range(len(steps)):
- self.gains.append(1 + self.gaind * self.y[i])
- self.biass.append(1 + self.biasd * self.y[i])
+ self.gains.append(self.gaind * self.y[i] + 1)
+ self.biass.append(self.biasd * self.y[i] + 1)
def plot(self):
out = f"./out/{self.__class__.__name__}"
#######################################################################
- title = "Concentration vs Gain Scalar in" + self.pretty
- logging.info("Plotting " + title)
- plt.figure()
- plt.plot(self.x, self.gains)
-
- plt.xlabel("Norepinephrine concentration (nM)")
- plt.ylabel("Gain")
- plt.title(title)
-
- plt.xscale("log")
-
- plt.draw()
- plt.savefig(f"{out}-concentration-gain.png")
-
- #######################################################################
-
- title = "Concentration vs Bias scalar in " + self.pretty
+ title = "Concentration vs Gain/Bias scalar in " + self.pretty
logging.info("Plotting " + title)
plt.figure()
- plt.plot(self.x, self.biass)
+ plt.plot(self.x, self.biass, label="Bias scalar")
+ plt.plot(self.x, self.gains, label="Gain scalar")
plt.xscale("log")
plt.xlabel("Norepinephrine concentration (nM)")
- plt.ylabel("Bias")
+ plt.ylabel("Level")
plt.title(title)
+ plt.legend()
plt.draw()
- plt.savefig(f"{out}-concentration-bias.png")
+ plt.savefig(f"{out}-concentration-bias-gains.png")
class Alpha1(Alpha):
def __init__(self):
self.ki = 330
self.offset = 5.895
- self.pretty = "α1 Receptor"
- self.gaind = -0.02
- self.biasd = 0.04
+ self.pretty = "α1"
+ #self.gaind = -0.02
+ self.gaind = -0.1
+ #self.biasd = 0.04
+ self.biasd = 0.1
super().__init__()
def __init__(self):
self.ki = 56
self.offset = 1
- self.pretty = "α2 Receptor"
+ self.pretty = "α2"
self.gaind = 0.1
self.biasd = -0.1
super().__init__()
class Simulation():
def __init__(self):
self.a1 = Alpha1()
+ self.a1.plot()
self.a2 = Alpha2()
- self.num_spikes = np.ones(len(steps))
- self.biass = np.ones(len(steps))
- self.gains = np.ones(len(steps))
- self.out = np.ones(3)
+ 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
- self.perceived = np.ones(3) # correctly perceived (not necessarily remembered) cues
- rng=np.random.RandomState(seed=111)
- self.cues=2 * rng.randint(2, size=3)-1 # whether the cues is on the left or right
+ # 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
plt.figure()
plt.plot(steps, self.num_spikes)
- plt.xscale("log")
-
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]
nengo.Connection(decision, output, function=decision_function)
# Probes
- probes_wm = nengo.Probe(wm[0], synapse=0.01)
- probe_spikes = nengo.Probe(wm.neurons)
- probe_output = nengo.Probe(output, synapse=None)
+ 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
- with nengo.Simulator(net, dt=dt, progress_bar=False) as sim:
- for i, _ in tqdm(enumerate(steps), total=len(steps)):
- 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].gain
- for self.trial in range(3):
- logging.debug(f"Simulating: trial: {self.trial}, gain: {fmt_num(self.gains[i])}, bias: {fmt_num(self.biass[i])}")
- sim.run(t_cue + t_delay)
- self.out[self.trial] = np.count_nonzero(sim.data[probe_spikes])
- self.num_spikes[i] = np.average(self.out)
+ 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.num_spikes, 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()
+
+
def main():
logging.info("Initializing simulation")
plt.style.use("ggplot") # Nice looking and familiar style
- Simulation().run()
+
+ try:
+ data = open("simulation.pkl", "rb")
+ except FileNotFoundError:
+ Simulation().run()
+ else:
+ pickle.load(data).plot()
if __name__ == "__main__":
pass
logging.basicConfig(filename=f"out/{datetime.now().isoformat()}.log",
- level=logging.DEBUG)
+ level=logging.INFO)
main()
projects / norepinephrine_wm.git / blobdiff