Computational modelling of learning and behaviour in small neuronal systems

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.<>