Learning in stochastic bit stream neural networks

This paper presents learning techniques for a novel feedforward stochastic neural network. The model uses stochastic weights and the “bit stream” data representation. It has a clean analysable functionality and is very attractive with its great potential to be implemented in hardware using standard digital VLSI technology. The design allows simulation at three different levels and learning techniques are described for each level. The lowest level corresponds to on-chip learning. Simulation results on three benchmark MONK's problems and handwritten digit recognition with a clean set of 500 16 × 16 pixel digits demonstrate that the new model is powerful enough for the real world applications. Copyright © 1996 Elsevier Science Ltd