Modeling neural networks in Scheme

Abstract

A system for simulating neural networks has been written in the LISP dialect, Scheme, using an object-oriented style of program ming, rather than the standard numerical techniques used in previous studies. Each node in the Scheme network represents either a neuron or a functional group of neurons, and can pass messages which trigger computations and actions in other nodes. The Scheme modeling approach overcomes two major problems inherent to the standard numerical approach. First, it provides a flexible environment for systematically studying the effects of perturbing a network's structure, response, or updating param eters. In fact, the Scheme system can recreate any previously studied neural network. Second, it allows the construction of hierarchical networks with several interacting levels. This system can handle hierarchical organization in a natural way, because a single node in a Scheme network can contain a model of an entire lower level of neural processing. The implementation of neural networks with hierarchical structure is significant because recent biological data suggests that this is the architecture which supports human cognition.