A neural computation model with short-term memory

A study of the memory characteristics of the brain and the computer prompts the creation of a new neuron architecture for neural computation. We hypothesize that neural responses resemble hysteresis loops. The upper and lower halves of the hysteresis loop are described by two sigmoids. Generalizing the two sigmoids to two families of curves accommodates loops of various sizes. This model, which we call the 'hystery model', is capable of memorizing the entire history of its bipolar inputs in an adaptive fashion, with larger memory for longer sequences. We theorize and prove that the hystery model's response converges asymptotically to hysteresis-like loops. A simple application to temporal pattern discrimination demonstrates the nonlinear short-term memory characteristics of the hystery model. This model may have important applications for time-based computations such as control, signal processing and spatiotemporal pattern recognition, especially if it can take advantage of existing hysteresis phenomena in semiconductor materials.<
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