Sparse random networks with LTP learning rules approximate Bayes classifiers via Parzen's method

Abstract Analysis is performed of a neuronal cell layer consisting of sparse random anatomical innervation, incorporating anatomically accurate distribution of excitatory and inhibitory cells, appropriate physiological time courses of excitatory-inhibitory interactions, and synaptic modification via long-term potentiation (LTP). One of two input pathways selects cells that learn and the other uses LTP-based learning rules to modify synapses of selected cells. It is found that the resulting network implements Parzen's method for approximating a Bayes classifier. The simple one-layer network can be generalized to include a class of networks including one that adds an input layer of neurons resulting in changes to the kernel used in the Parzen method. The LTP learning rule is nondecremental and limited to a maximum ceiling, which will result in essential saturation of synaptic weights; nonetheless, calculations using reasonable estimates of network parameters indicate a large network capacity.