Temporal sequence processing based on the biological reaction-diffusion process

Temporal (spatiotemporal) sequences are a fundamental form of information and communication both in natural and engineered systems. The biological control process which directs the generation of iterative structures from undifferentiated tissue is a type of temporal sequential process. A quantitative explanation of this temporal process is reaction-diffusion, initially proposed by Taring in 1952 and later widely studied and elaborated. We have adapted the reaction-diffusion mechanism to create a novel network and algorithm based on a chemical “neuron” model, which performs storage, associative retrieval and prediction for temporal sequences. Experiments demonstrate the ability of the device to achieve any desired depth to resolution ratio, limited only by storage capacity, to remember and predict on the basis of count to any length, and to learn an embedded Reber grammar to arbitrary accuracy and permit retrieval with controllable redundancy