Purpose: This demo shows how to construct and manipulate a single neuron.
Comments: This leaky integrate-and-fire (LIF) neuron is a simple, standard model of a spiking single neuron. It resides inside a neural ‘population’, even though there is only one neuron.
Usage: Grab the slider control and move it up and down to see the effects of increasing or decreasing input. This neuron will fire faster with more input (an ‘on’ neuron).
Output: See the screen capture below
import nef net=nef.Network('Single Neuron') # Create the network net.make_input('input',[-0.45]) # Create a controllable input # with a starting value of -.45 net.make('neuron',neurons=1,dimensions=1, # Make 1 neuron representing max_rate=(100,100),intercept=(-0.5,-0.5), # 1 dimension, with a maximum encoders=[],noise=3) # firing rate of 100, with a # tuning curve x-intercept of # -0.5, encoder of 1 (i.e. it # responds more to positive # values) and a noise of # variance 3 net.connect('input','neuron') # Connect the input to the neuron net.add_to_nengo()