Purpose: This demo shows how to construct and manipulate a complementary pair of neurons.
Comments: These are leaky integrate-and-fire (LIF) neurons. The neuron tuning properties have been selected so there is one ‘on’ and one ‘off’ neuron.
Usage: Grab the slider control and move it up and down to see the effects of increasing or decreasing input. One neuron will increase for positive input, and the other will decrease. This can be thought of as the simplest population to give a reasonable representation of a scalar value.
Output: See the screen capture below
import nef net=nef.Network('Two Neurons') # Create the network net.make_input('input',[-0.45]) # Create a controllable input # with a starting value of -.45 net.make('neuron',neurons=2,dimensions=1, # Make 2 neurons representing max_rate=(100,100),intercept=(-0.5,-0.5), # 1 dimension, with a maximum encoders=[,[-1]],noise=3) # firing rate of 100, with a # tuning curve x-intercept of # -0.5, encoders of 1 and -1 # (i.e. the first responds more # to positive values and the # second to negative values), # and a noise of variance 3 net.connect('input','neuron') # Connect the input to the neuron net.add_to_nengo()