-import numpy as np
-import matplotlib.pyplot as plt
+from datetime import datetime
from os import mkdir
+import logging
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
+import nengo
+import numpy as np
+
+exec(open("conf.py").read())
+
+
+def fmt_num(num, width=18):
+ """
+ Format number to string.
+ """
+
+ return str(num)[:width].ljust(width)
+
+
+def inputs_function(x):
+ return x * tau_wm
+
+
+def noise_decision_function(t):
+ return np.random.normal(0.0, noise_decision)
+
+
+def noise_bias_function(t):
+ return np.random.normal(0.0, noise_wm)
+
+
+def time_function(t):
+ return time_scale if t > t_cue else 0
+
+
+def decision_function(x):
+ return 1.0 if x[0] + x[1] > 0.0 else -1.0
-# Refer to parameters.txt for documentation
-dt = 0.001
-t_cue = 1.0
-cue_scale = 1.0
-perceived = 0 # ???
-time_scale = 0.4
-steps = 100
class Alpha(object):
+ """
+ Base class for alpha receptors. Not to be used directly.
+ """
+
def __init__(self):
self.x = np.logspace(0, 3, steps)
self.y = 1 / (1 + (999 * np.exp(-0.1233 * (self.x / self.offset))))
-
- self.gain = []
- self.bias = []
-
- def calcgb(self, gaind, biasd):
+
+ self.gains = []
+ self.biass = []
+
for i in range(steps):
y = self.y[i]
- self.gain.append(1 + gaind * y)
- self.bias.append(1 + biasd * y)
+ self.gains.append(1 + self.gaind * y)
+ self.biass.append(1 + self.biasd * y)
def plot(self):
- try:
- mkdir("./out")
- except FileExistsError:
- pass
-
out = f"./out/{self.__class__.__name__}"
+
+ title = "Norepinepherine Concentration vs Neuron Activity in " + self.pretty
+ logging.info("Plotting " + title)
+ plt.figure()
plt.plot(self.x, self.y)
plt.xlabel("Norepinephrine concentration (nM)")
plt.ylabel("Activity (%)")
- plt.title("Norepinepherine Concentration vs Neuron Activity in " +
- self.pretty)
+ plt.title(title)
plt.vlines(self.ki, 0, 1, linestyles="dashed")
plt.text(1.1 * self.ki, 0.1, "Affinity")
plt.text(1, 0.51, "50%")
plt.xscale("log")
- gc = plt.gca()
- gc.set_yticklabels(['{:.0f}%'.format(x * 100) for x in gc.get_yticks()])
+ plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.draw()
- plt.savefig(f"{out}-norep-activity.png", dpi=1000)
-
+ plt.savefig(f"{out}-norep-activity.png")
+
#######################################################################
-
- plt.plot(self.x, self.gain)
-
+
+ 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(f"Concentration vs Gain in {self.pretty}")
+ plt.title(title)
+
+ plt.xscale("log")
plt.draw()
- plt.savefig(f"{out}-concentration-gain.png", dpi=1000)
-
+ plt.savefig(f"{out}-concentration-gain.png")
+
#######################################################################
-
- plt.plot(self.x, self.bias)
-
+
+ title = "Concentration vs Bias scalar in " + self.pretty
+ logging.info("Plotting " + title)
+ plt.figure()
+ plt.plot(self.x, self.biass)
+
+ plt.xscale("log")
+
plt.xlabel("Norepinephrine concentration (nM)")
plt.ylabel("Bias")
- plt.title("Concentration vs Bias in " + self.pretty)
-
+ plt.title(title)
+
plt.draw()
- plt.savefig(f"{out}-concentration-bias.png", dpi=1000)
+ plt.savefig(f"{out}-concentration-bias.png")
+
class Alpha1(Alpha):
+ """
+ Subclass of Alpha representing an alpha1 receptor.
+ """
+
def __init__(self):
self.ki = 330
self.offset = 5.895
self.pretty = "α1 Receptor"
- self.gaind = 0.1
- self.biasd = 0.1
+ self.gaind = -0.02
+ self.biasd = 0.04
super().__init__()
-
- def calcgb(self):
- super().calcgb(self.gaind, self.biasd)
+
class Alpha2(Alpha):
+ """
+ Subclass of Alpha representing an alpha2 receptor.
+ """
+
def __init__(self):
self.ki = 56
self.offset = 1
self.pretty = "α2 Receptor"
- self.gaind = -0.04
- self.biasd = -0.02
+ self.gaind = 0.1
+ self.biasd = -0.1
super().__init__()
-
- def calcgb(self):
- super().calcgb(self.gaind, self.biasd)
+
+
+def simulate(a1, a2):
+ for i in range(steps):
+ gain = a1.gains[i] + a2.gains[i] - 1
+ bias = a1.biass[i] + a2.biass[i] - 1
+ logging.info(f"gain: {fmt_num(gain)}, bias: {fmt_num(bias)}")
+ 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, gain)
+ wm.bias = np.full(wm.n_neurons, bias)
+ 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)
+ 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)
+ probe_output = nengo.Probe(output, synapse=None)
+
+ # Run simulation
+ with nengo.Simulator(net, dt=dt, progress_bar=False) as sim:
+ sim.run(t_cue + t_delay)
+
def main():
- plt.style.use("ggplot")
-
+ logging.info("Initializing simulation")
+ plt.style.use("ggplot") # Nice looking and familiar style
+
a1 = Alpha1()
- a1.calcgb()
a1.plot()
a2 = Alpha2()
- a2.calcgb()
a2.plot()
+ simulate(a1, a2)
+
+
if __name__ == "__main__":
+ try:
+ mkdir("./out")
+ except FileExistsError:
+ pass
+
+ logging.basicConfig(filename=f"out/{datetime.now().isoformat()}.log",
+ level=logging.DEBUG)
+ console = logging.StreamHandler()
+ console.setLevel(logging.INFO)
+ logging.getLogger("").addHandler(console)
+
main()
projects / norepinephrine_wm.git / blobdiff