Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks

TL;DR

This paper introduces a new input-driven dynamical framework for Hopfield networks that enhances memory retrieval robustness by directly influencing neural synapses and energy landscapes, especially under noisy conditions.

Contribution

It presents a novel plasticity-based model that incorporates external inputs into Hopfield networks, improving understanding of memory retrieval and robustness.

Findings

Enhanced classification of mixed inputs

Improved robustness to noise during retrieval

Unified framework with modern Hopfield architectures

Abstract

The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of external inputs has been largely underexplored, from their effects on neural dynamics to how they facilitate effective memory retrieval. To bridge this gap, we propose a novel dynamical system framework in which the external input directly influences the neural synapses and shapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of the memory retrieval process and proves effective at correctly classifying highly mixed inputs. Furthermore, we integrate this model within the framework of modern Hopfield architectures, using this connection to elucidate how current and past information are combined during the retrieval process. Finally, we embed both the classic and the new model in an environment disrupted by noise and compare their robustness during memory retrieval.