Recognition Capabilities of a Hopfield Model with Auxiliary Hidden Neurons
Disordered Systems and Neural Networks
2021-06-16 v1 Statistical Mechanics
Biological Physics
Neurons and Cognition
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 in a regime of complete confusion when memory load exceeds a sharp threshold.
Keywords
Cite
@article{arxiv.2101.05247,
title = {Recognition Capabilities of a Hopfield Model with Auxiliary Hidden Neurons},
author = {Marco Benedetti and Victor Dotsenko and Giulia Fischetti and Enzo Marinari and Gleb Oshanin},
journal= {arXiv preprint arXiv:2101.05247},
year = {2021}
}