Effect of spatial correlations on Hopfield Neural Network and Dense Associative Memories

Giordano De Marzo and Giulio Iannelli

Physica A: Statistical Mechanics and its Applications, 2023, vol. 612, issue C

Abstract: Hopfield model is one of the few neural networks for which analytical results can be obtained. However, most of them are derived under the assumption of random uncorrelated patterns, while in real life applications data to be stored show non-trivial correlations. In the present paper we study how the retrieval capability of the Hopfield network at null temperature is affected by spatial correlations in the data we feed to it. In particular, we use as patterns to be stored the configurations of a linear Ising model at inverse temperature β thus limiting our analysis to exponentially decaying spatial correlations. Exploiting the signal to noise technique we obtain a phase diagram in the load of the Hopfield network and the Ising temperature where a fuzzy phase and a retrieval region can be observed. Remarkably, as the spatial correlation inside patterns is increased, the critical load of the Hopfield network diminishes, a result also confirmed by numerical simulations. The analysis is then generalized to Dense Associative Memories with arbitrary odd-body interactions, for which we obtain analogous results.

Keywords: Hopfield neural network; Dense associative memories; Signal to noise analysis (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:612:y:2023:i:c:s0378437123000420

DOI: 10.1016/j.physa.2023.128487

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