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Testing a computational model of biological neural networks

James T. Lo, Department of Mathematics and Statistics, UMBC
Bryce Carey, Department of Mathematics and Statistics, UMBC
David Alexander, Department of Mathematics and Statistics, UMBC

A computational model of neural networks was proposed that is a recurrent network of processing units each comprising new models of dendritic trees, synapses, spiking/nonspiking somas, unsupervised/supervised learning mechanisms, and a maximal generalization scheme. The model shows how neural networks encode, learn, memorize, recall and generalize.

In the project, we will test these capabilities and evaluate the model as a learning machine for pattern clustering, detection, recognition and localization on large benchmark data sets. Comparison with prior learning machines will be performed.