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arXiv · 2505.06902

Neuromodulation via Krotov-Hopfield Improves Accuracy and Robustness of RBMs

Abstract

In biological systems, neuromodulation tunes synaptic plasticity based on the internal state of the organism, complementing stimulus-driven Hebbian learning. The algorithm recently proposed by Krotov and Hopfield \cite{krotov_2019} can be utilized to mirror this process in artificial neural networks, where its built-in intra-layer competition and selective inhibition of synaptic updates offer a cost-effective remedy for the lack of lateral connections through a simplified attention mechanism. We demonstrate that KH-modulated RBMs outperform standard (shallow) RBMs in both reconstruction and classification tasks, offering a superior trade-off between generalization performance and model size, with the additional benefit of robustness to weight initialization as well as to overfitting during training.

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Başer Tambaş, A. Levent Subaşı, Alkan Kabakçıoğlu. 2025-05-11. Neuromodulation via Krotov-Hopfield Improves Accuracy and Robustness of RBMs. https://doi.org/10.1103/y82t-c34b

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