Notebook

# Nengo Example: Inhibitory Gating of Ensembles

In [1]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import nengo
<IPython.core.display.Javascript at 0x7fdd68f80610>

## Step 1: Create the network

Our model consists of two ensembles (called A and B) that receive inputs from a common sine wave signal generator.

Ensemble A is gated using the output of a node, while Ensemble B is gated using the output of a third ensemble (C). This is to demonstrate that ensembles can be gated using either node outputs, or decoded outputs from ensembles.

In [2]:
n_neurons = 30

model = nengo.Network(label="Inhibitory Gating")
with model:
A = nengo.Ensemble(n_neurons, dimensions=1)
B = nengo.Ensemble(n_neurons, dimensions=1)
C = nengo.Ensemble(n_neurons, dimensions=1)

## Step 2: Provide input to the model

As described in Step 1, this model requires two inputs.

1. A sine wave signal that is used to drive ensembles A and B
2. An inhibitory control signal used to (directly) gate ensemble A, and (indirectly through ensemble C) gate ensemble B.
In [3]:
from nengo.utils.functions import piecewise

with model:
sin = nengo.Node(np.sin)
inhib = nengo.Node(piecewise({0: 0, 2.5: 1, 5: 0, 7.5: 1, 10: 0, 12.5: 1}))

## Step 3: Connect the different components of the model

In this model, we need to make the following connections:

1. From sine wave generator to Ensemble A
2. From sine wave generator to Ensemble B
3. From inhibitory control signal to the neurons of Ensemble A (to directly drive the currents of the neurons)
4. From inhibitory control signal to Ensemble C
5. From Ensemble C to the neurons of Ensemble B (this demonstrates that the decoded output of Ensemble C can be used to gate Ensemble B)
In [4]:
with model:
nengo.Connection(sin, A)
nengo.Connection(sin, B)
nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)
nengo.Connection(inhib, C)
nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)

## Step 4: Probe outputs

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later. Let's collect all the data produced.

In [5]:
with model:
sin_probe = nengo.Probe(sin)
inhib_probe = nengo.Probe(inhib)
A_probe = nengo.Probe(A, synapse=0.01)
B_probe = nengo.Probe(B, synapse=0.01)
C_probe = nengo.Probe(C, synapse=0.01)

## Step 5: Run the model

In order to run the model, we have to create a simulator. Then, we can run that simulator over and over again without affecting the original model.

In [6]:
# Create our simulator
with nengo.Simulator(model) as sim:
# Run it for 15 seconds
sim.run(15)
In [7]:
# Plot the decoded output of Ensemble A
plt.figure()
plt.plot(sim.trange(), sim.data[A_probe], label='Decoded output')
plt.plot(sim.trange(), sim.data[sin_probe], label='Sine input')
plt.plot(sim.trange(), sim.data[inhib_probe], label='Inhibitory signal')
plt.legend();
In [8]:
# Plot the decoded output of Ensemble B and C
plt.figure()
plt.plot(sim.trange(), sim.data[B_probe], label='Decoded output of B')
plt.plot(sim.trange(), sim.data[sin_probe], label='Sine input')
plt.plot(sim.trange(), sim.data[C_probe], label='Decoded output of C')
plt.plot(sim.trange(), sim.data[inhib_probe], label='Inhibitory signal')
plt.legend();