Lateral inhibition is the most commonly known form of computation by neural networks. As its name implies, lateral inhibitory architectures are characterized by inhibition off to the sides of some form of straight ahead excitation.
Lateral inhibition finds many uses throughout the brain.
In this assignment you will study computational models of lateral inhibitory networks that process signals spatially and temporally. This and all subsequent computer programming assignments in Part II will involve matrix computation. Some m-file printouts that you will find useful are given on the last page of this assignment. The central concern of network level modeling is not the neural elements themselves, which are usually quite simple, but the interconnections of those elements. If the states of the units are represented as vectors, then the weights connecting the layers can be represented in a matrix.