Latent Structured Hopfield Network for Semantic Association and Retrieval

2025-06-02

AI Summary

This study addresses the neural modeling of dynamic binding and associative retrieval of semantic elements—objects, locations, and temporal information—in episodic memory. We propose a biologically inspired latent-structure Hopfield network that, for the first time, embeds continuous Hopfield attractor dynamics within an end-to-end trainable encoder–latent-attractor–decoder framework. This architecture enables dynamic semantic binding and robust associative recall, bridging the computational gap between neocortical and hippocampal mechanisms. Memory convergence is guided by attractor dynamics in the latent space, while a noise- and occlusion-robust reconstruction mechanism enhances resilience. Evaluated on MNIST, CIFAR-10, and synthetic episodic memory tasks, our model achieves significantly higher recall accuracy under input corruption (e.g., occlusion and additive noise) compared to state-of-the-art associative memory models.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Episodic memory enables humans to recall past experiences by associating semantic elements such as objects, locations, and time into coherent event representations. While large pretrained models have shown remarkable progress in modeling semantic memory, the mechanisms for forming associative structures that support episodic memory remain underexplored. Inspired by hippocampal CA3 dynamics and its role in associative memory, we propose the Latent Structured Hopfield Network (LSHN), a biologically inspired framework that integrates continuous Hopfield attractor dynamics into an autoencoder architecture. LSHN mimics the cortical-hippocampal pathway: a semantic encoder extracts compact latent representations, a latent Hopfield network performs associative refinement through attractor convergence, and a decoder reconstructs perceptual input. Unlike traditional Hopfield networks, our model is trained end-to-end with gradient descent, achieving scalable and robust memory retrieval. Experiments on MNIST, CIFAR-10, and a simulated episodic memory task demonstrate superior performance in recalling corrupted inputs under occlusion and noise, outperforming existing associative memory models. Our work provides a computational perspective on how semantic elements can be dynamically bound into episodic memory traces through biologically grounded attractor mechanisms.

Problem

• Modeling associative structures for episodic memory formation
• Integrating Hopfield dynamics into autoencoder for memory retrieval
• Improving recall of corrupted inputs under noise and occlusion

Innovation

• Latent Structured Hopfield Network for associative memory
• End-to-end trained with gradient descent
• Biologically inspired attractor mechanisms for retrieval