We study the recognition capabilities of the Hopfield model with auxiliary hidden layers, which emerge naturally upon a Hubbard-Stratonovich transformation. We show that the recognition capabilities of such a model at zero temperature outperform those of the original Hopfield model, due to a substantial increase of the storage capacity and the lack of a naturally defined basin of attraction. The modified model does not fall abruptly into the regime of complete confusion when memory load exceeds a sharp threshold. This latter circumstance, together with an increase of the storage capacity, renders such a modified Hopfield model a promising candidate for further research, with possible diverse applications.

Recognition capabilities of a Hopfield model with auxiliary hidden neurons / Benedetti, M.; Dotsenko, V.; Fischetti, G.; Marinari, E.; Oshanin, G.. - In: PHYSICAL REVIEW. E. - ISSN 2470-0045. - 103:6(2021). [10.1103/PhysRevE.103.L060401]

Recognition capabilities of a Hopfield model with auxiliary hidden neurons

Benedetti M.;Fischetti G.;Marinari E.
;
2021

Abstract

We study the recognition capabilities of the Hopfield model with auxiliary hidden layers, which emerge naturally upon a Hubbard-Stratonovich transformation. We show that the recognition capabilities of such a model at zero temperature outperform those of the original Hopfield model, due to a substantial increase of the storage capacity and the lack of a naturally defined basin of attraction. The modified model does not fall abruptly into the regime of complete confusion when memory load exceeds a sharp threshold. This latter circumstance, together with an increase of the storage capacity, renders such a modified Hopfield model a promising candidate for further research, with possible diverse applications.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1577265
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