MULTI-LAYER NEURAL NETWORK EMPLOYING MULTIPLEXED OUTPUT NEURONS

A multi-layer electrically trainable analog neural network employing multiplexed output neurons having inputs organized into two groups, external and recurrent (i.e., feedback). Each layer of the network comprises a matrix of synapse cells which implement a matrix multiplication between an input vector and a weight matrix. In normal operation, an external input vector coupled to the first synaptic array generates a Sigmoid response at the output of a set of neurons. This output is then fed back to the next and subsequent layers of the network as a recurrent input vector. The output of second layer processing is generated by the same neurons used in first layer processing. Thus, the neural network of the present invention can handle N-layer operation by using recurrent connections and a single set of multiplexed output neurons.