UMBC High Performance Computing Facility

Testing a computational model of biological neural
networks

James T. Lo, Department of Mathematics and Statistics, UMBCBryce 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.