The probabilistic RAM (pRAM) is a hardware-realizable neural device which is stochastic in operation and highly nonlinear. Even small nets of pRAMs offer high levels of functionality. The means by which a pRAM network generalizes when trained in noise is shown and the results of this behavior are described.

The pRAM (probabilistic RAM) is a nonlinear stochastic device with neuron like behavior. The pRAM is realizable in hardware, and the third-generation VLSI pRAM chip is described. This chip is adaptive since learning algorithms have been incorporated on-chip, using reinforcement training. The pRAM chip is also adaptive with respect to the interconnections between neurons. Results achieved from a small net of pRAM's performing a pattern-recognition task using reinforcement training are presented.

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