Population of NeuronsΒΆ

Purpose: This demo shows how to construct and manipulate a population of neurons.

Comments: These are 100 leaky integrate-and-fire (LIF) neurons. The neuron tuning properties have been randomly selected.

Usage: Grab the slider control and move it up and down to see the effects of increasing or decreasing input. As a population, these neurons do a good job of representing a single scalar value. This can be seen by the fact that the input graph and neurons graphs match well.

Output: See the screen capture below

import nef

net=nef.Network('Many Neurons')       # Create the network
net.make_input('input',[-0.45])       # Create a controllable input
                                      #   with a starting value of -.45

net.make('neurons',neurons=100,       # Make a population of 100 neurons, 
           dimensions=1,noise=1)      #  representing 1 dimensions with random
                                      #  injected input noise of variance 1

net.connect('input','neurons')        # Connect the input to the neuron

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