Proceedings of the Annual Meeting of the Cognitive Science Society

The synapse plays a fundamental role in the computations performed by the brain. The excitatory or inhibitory nauire of a synapse represents a (simplified) characterization of both the synapse itself and the computational role it plays in the larger circuit. M u ch speculation concerns the functional importance of excitation and inhibition in the physiology of the cerebral cortex. The current study uses neural network (connectionist) models to ask whether or not the relative proportion of inhibition (i.e., inhibitory synapses) and excitation (i.e., excitatory synapses) in the brain affects the development of its neural networks? The results are affirmative: A n artificial neural network, designed to perform a particular task involving winner-take-all output nodes, is sensitive to the initial configuration of positive (excitatory) and negative (inhibitory) connections (synapses), such that it learns considerably faster when started with 60-75% inhibitory connections than when it includes a greater or lesser proportion than this. Implications of this result for neuroanatomy and neurophysiology are discussed.