Computational modelling of learning and behaviour in small neuronal systems

Abstract

It is noted that almost all attempts to model neural and brain function have fallen into one of two categories: artificial neural networks using (ideally) large numbers of simple but densely interconnected processing elements, or detailed physiological models of single neurons. The authors report on their progress in formulating a computational model which functions at a level between these two extremes. Individual neurons are considered at the level of membrane potential; this allows outputs from the model to be compared directly with physiological data obtained in intracellular recording. An object-oriented programming language has been used to produce a model where each object equates to a neuron. The benefits of using an object-oriented language are two-fold. The program has been tested by modeling the learning and behavior of the gill-withdrawal reflex in Aplysia. The use of a parameter-based system has made it possible to specify appropriate characteristics for the particular neurons participating in this reflex and to simulate some of the subcircuits involved.<>