Chaotic Resonance Theory, a New Approach for Pattern Storage and Retrieval in Neural Networks 1 Radu Dogaru, A.T. Murgan “Politehnica” University of Bucharest, Applied Electronics Department, Spl. Independentei Nr. 313, Sector 6, Bucharest, ROMANIA, Tel: +40-1-4105400/ ext. 140 E-Mail: radu_d@lmn.pub.ro ; atmurgan@vala.elia.pub.ro 1 Published in ICNN’95 Proceedings, Vol. 6, pp. 3048-3052 ABSTRACT 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. 1. Introduction Pattern storage and retrieval is one of the most important feature of the artificial neural networks. While it is not yet clear how this process is achieved in brains, there are a lot of studies and models for artificial neural networks memories. According with some opinions [7] chaos can play an important role in the process of storage and retrieval of information. The Hopfield model considers the prototype patterns (to be stored) as attractors in the state space of a neural system with symmetric weights. Generally speaking, they can be viewed as centroids which best characterize all the patterns belonging to a cluster centered on the prototype pattern associated with the centroid. Simple learning rule based on Hebb's law assure patterns storage while the retrieval is based on fast convergence to the closest fixed point solution, considering the input pattern presented as the initial condition. The clusters associated with a pattern are thus basins of attraction. There are however some disadvantages related with spurious attractors and low memory capacity. There is no possibility to control the radius of each cluster, and thus some overlapping phenomena can occur. The ART theory [1] which is much inspired from neuro-biology and neuro- psychology gives solutions to overcome the major drawbacks of the Hopfield associative memory model, being adaptive to new stimuli due to an unsupervised built-in learning mechanism. The "vigilance" parameter allows to globally control the clusters radius. The clustering process occur in a competitive layer of standard neurons and the learned information is stored in the synaptic weights between the two major layers of such a network. In this paper we introduce the Chaotic Resonance Theory (CRT) as a new principle for pattern storage and retrieval, essentially based on exploiting the phenomena of chaos synchronization [5]. A completely new neural architecture based on CRT is presented. This architecture can be used for clustering, having a behavior almost similar with the ART neural networks, while the different principle used to achieve this behavior gives some opportunities regarding the VLSI implementation and . 2. The Chaotic Resonance Theory neural network 2.1. Architecture and operation principle According to Fig.1, the architecture of a Chaotic Resonance Theory (CRT) neural network is