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 net.add_to_nengo()