Write outer loop

diff --git a/conf.py b/conf.py
index a3b0603d36b0bc43a6ae0551920a79676a39b3dd..0c71dfa46faff3c0b775b8f55e3fbe47e966be55 100644 (file)
--- a/conf.py
+++ b/conf.py
@@ -1,15 +1,15 @@
-dt = 0.001  # Time step
-t_cue = 1.0  # Duration of cue presentation
-cue_scale = 1.0  # How strong the cuelus is from the visual system
-perceived = 0  # ???
-time_scale = 0.4
-steps = 100  # How fine to run the outer loop of the simulation
-noise_wm = 0.005  # Standard deviation of white noise added to workin memory
-noise_decision = 0.005  # Standard deviation of white noise added to decision
-neurons_decide = 100  # Number of neurons for decision
-neurons_inputs = 100  # Number of neurons for inputs ensemble
-neurons_wm = 100  # Number of neurons for working memory ensemble
-tau_wm = 0.1  # Synapse on recurrent connection in wm
-tau = 0.01  # Synaptic time constant between ensemblesA
-dt_sample = 0.01  # Timestep for data recording through probes
-t_delay = 8.0 # Duration of delay period between cue and decision
+dt = 0.001             # Time step
+t_cue = 1.0            # Duration of cue presentation
+cue_scale = 1.0        # How strong the cuelus is from the visual system
+perceived = 0          # ???
+time_scale = 0.4       # ???
+steps = 100            # How fine to run the outer loop of the simulation
+noise_wm = 0.005       # Standard deviation of white noise added to WM
+noise_decision = 0.005 # Standard deviation of white noise added to decision
+neurons_decide = 100   # Number of neurons for decision
+neurons_inputs = 100   # Number of neurons for inputs ensemble
+neurons_wm = 100       # Number of neurons for working memory ensemble
+tau_wm = 0.1           # Synapse on recurrent connection in wm
+tau = 0.01             # Synaptic time constant between ensembles
+dt_sample = 0.01       # Timestep for data recording through probes
+t_delay = 8.0          # Duration of delay period between cue and decision

diff --git a/model.py b/model.py
index db9946a45aacd09ca174a2a65437b3bae8a35982..f3079e211decc92113a2d04546e4f0c5df178a34 100644 (file)
--- a/model.py
+++ b/model.py
@@ -6,20 +6,28 @@ import numpy as np
 
 exec(open("conf.py").read())
 
+def fmt_num(num):
+    """
+    Format number to string.
+    """
+
+    return str(num)[:18].zfill(18)
+
+
 def wm_recurrent_function(x):
-       return x
+    return x
 
 
 def inputs_function(x):
-       return x * tau_wm
+    return x * tau_wm
 
 
 def noise_decision_function(t):
-       return np.random.normal(0.0, noise_decision)
+    return np.random.normal(0.0, noise_decision)
 
 
 def noise_bias_function(t):
-       return np.random.normal(0.0, noise_wm)
+    return np.random.normal(0.0, noise_wm)
 
 
 def time_function(t):
@@ -54,6 +62,7 @@ class Alpha(object):
             pass
 
         out = f"./out/{self.__class__.__name__}"
+        plt.figure()
         plt.plot(self.x, self.y)
 
         plt.xlabel("Norepinephrine concentration (nM)")
@@ -75,6 +84,7 @@ class Alpha(object):
 
         #######################################################################
 
+        plt.figure()
         plt.plot(self.x, self.gain)
 
         plt.xlabel("Norepinephrine concentration (nM)")
@@ -86,6 +96,7 @@ class Alpha(object):
 
         #######################################################################
 
+        plt.figure()
         plt.plot(self.x, self.bias)
 
         plt.xlabel("Norepinephrine concentration (nM)")
@@ -96,6 +107,8 @@ class Alpha(object):
         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)
@@ -105,6 +118,8 @@ class Alpha(object):
 
                 # 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)
@@ -124,16 +139,14 @@ class Alpha(object):
                 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)
+                # 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) as sim:
-                    sim.run(t_cue + t_delay)
+            with nengo.Simulator(net, dt=dt, progress_bar=False) as sim:
+                sim.run(t_cue + t_delay)
 
 
 class Alpha1(Alpha):
@@ -145,8 +158,8 @@ class Alpha1(Alpha):
         self.ki = 330
         self.offset = 5.895
         self.pretty = "α1 Receptor"
-        self.gaind = 0.1
-        self.biasd = 0.1
+        self.gaind = -0.04
+        self.biasd = -0.02
         super().__init__()
 
 
@@ -159,20 +172,20 @@ class Alpha2(Alpha):
         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 main():
     plt.style.use("ggplot")  # Nice looking and familiar style
-
     a1 = Alpha1()
     # a1.plot()
     a1.simulate()
 
-    #a2 = Alpha2()
+    a2 = Alpha2()
     # a2.plot()
+    a2.simulate()
 
 
 if __name__ == "__main__":