Implementing neural networks

This dissertation reviews theoretical aspects of hardware implementations of neurally inspired algorithms. It examines various methods of implementing Back Propagation, the difficulties associated with its implementation, including precision, offset errors, and general scaling issues. It proposes several methods for implementing Back Propagation. The dissertation closes with the derivation of a new algorithm which overcomes several of the most serious problems.