Nengo Example: A Single Neuron

This demo shows you how to construct and manipulate a single leaky integrate-and-fire (LIF) neuron. The LIF neuron is a simple, standard neuron model, and here it resides inside a neural ‘population’, even though there is only one neuron.

Step 1: Create the Neuron



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import nengo
from nengo.objects import Uniform
model = nengo.Network(label='A Single Neuron')
with model:
    neuron = nengo.Ensemble(nengo.LIF(1),
                            dimensions=1, # Represent a scalar
                            intercepts=Uniform(-.5, -.5),  # Set intercept to 0.5
                            max_rates=Uniform(100, 100),  # Set the maximum firing rate of the neuron to 100hz
                            encoders=[[1]])  # Sets the neurons firing rate to increase for positive input

Step 2: Provide Input to the Model

Create an input node generating a cosine wave.



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import numpy as np
with model:
    cos = nengo.Node(lambda t: np.cos(8 * t))

Step 3: Connect the Network Elements



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with model:
    # Connect the input signal to the neuron
    nengo.Connection(cos, neuron)

Step 4: Add Probes

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



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with model:
    cos_probe = nengo.Probe(cos, 'output')  # The original input
    spikes = nengo.Probe(neuron, 'spikes')  # The raw spikes from the neuron
    # voltages = nengo.Probe(neuron, 'voltages')  # Subthreshold soma voltage of the neuron  # TODO
    filtered = nengo.Probe(neuron, 'decoded_output', synapse=0.01) # Spikes filtered by a 10ms post-synaptic filter

Step 5: Run the Model



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sim = nengo.Simulator(model) # Create the simulator
sim.run(1) # Run it for 1 seconds

Step 6: Plot the Results



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import matplotlib.pyplot as plt

# Plot the decoded output of the ensemble
plt.plot(sim.trange(), sim.data[filtered])
plt.plot(sim.trange(), sim.data[cos_probe])
plt.xlim(0, 1)

# Plot the spiking output of the ensemble
from nengo.utils.matplotlib import rasterplot
plt.figure(figsize=(10, 8))
plt.subplot(221)
rasterplot(sim.trange(), sim.data[spikes])
plt.ylabel("Neuron")
plt.xlim(0, 1)

# Plot the soma voltages of the neurons
#plt.subplot(222)
#plt.plot(t, sim.data['Neuron.voltages'][:,0], 'r')
#plt.xlim(0, 1)

The top graph shows that the input signal in green and the filtered output spikes from the single neuron population in blue. The spikes (that are filtered) from the neuron are shown in the bottom graph on the left. On the right is the subthreshold voltages for the neuron.