Stochastic artifical neuron with multilayer training capability

A probabilistic or stochastic artificial neuron in which the inputs and synaptic weights are represented as probabilistic or stochastic functions of time, thus providing efficient implementations of the synapses. Stochastic processing removes both the time criticality and the discrete symbol nature of traditional digital processing, while retaining the basic digital processing technology. This provides large gains in relaxed timing design constraints and fault tolerance, while the simplicity of stochastic arithmetic allows for the fabrication of very high densities of neurons. The synaptic weights are individually controlled by a backward error propagation which provides the capability to train multiple layers of neurons in a neural network.