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Chaotic Resonance Theory, a New Approach for Pattern Storage and Retrieval in Neural Networks

A new architecture and methods for information storage in neural networks are presented. Behaving as Adaptive Resonance Theory neural networks, the proposed architecture is based on a different operation principle called Chaos Resonance Theory . According to this theory, standard neural units were replaced with small recurrent neural networks with chaotic dynamics, placed in a two layer architecture. The storage and retrieval of patterns are essentially based on chaos synchronization, and there are very few connections between units and layers, the architecture being attractive for VLSI implementation. Numerical simulations proved the possibility to store high amounts of patterns using relatively few units and synapses, the proposed architecture being also a plausible model for the neuro biological function of memory. The chaotic coding of information could also explain telepathic phenomena.