Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Neural Network Under External Stimulus: Improving Storage Capacity and Reactions

Version 1 : Received: 6 August 2020 / Approved: 7 August 2020 / Online: 7 August 2020 (11:25:33 CEST)

How to cite: Curado, E.; Melgar, N.; Nobre, F. Neural Network Under External Stimulus: Improving Storage Capacity and Reactions. Preprints 2020, 2020080189 (doi: 10.20944/preprints202008.0189.v1). Curado, E.; Melgar, N.; Nobre, F. Neural Network Under External Stimulus: Improving Storage Capacity and Reactions. Preprints 2020, 2020080189 (doi: 10.20944/preprints202008.0189.v1).

Abstract

Motivated by the way animals react in nature, we introduce a fully-connected neural-network model, which incorporates the external pattern presented to the system as a fundamental tool of the recognition process. In this new scenario, in the absence of this external field, memories are not attractors inside basins of attraction, as in usual attractor neural networks, although basins may be created according to the external pattern, thus allowing storing a much larger number of memories. The key point consists in calibrating the influence of the external pattern such as to cancel the noise generated by those memories not correlated with the external pattern.We illustrate how this proposal works by including this new contribution in the standard Hopfield model, showing a significant increase in its recognition capacity (typically by a factor $10^{2}$). As an additional feature of this model, we show its ability to react promptly to changes in the external environment, a crucial attribute of living beings. This procedure can be applied to a wide variety of neural-network models to increase considerably their recognition and reaction capabilities.

Subject Areas

Neural networks, Nonlinear Dynamical Systems, Pattern Recognition, Numerical Simulations

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