ArticleEntropy (Basel, Switzerland)2024
In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Heuristically Adaptive Diffusion-Model Evolutionary Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Information Dynamics of Generative Diffusion.Entropy (Basel, Switzerland) · 2026Article
- Explosive neural networks via higher-order interactions in curved statistical manifolds.Nature communications · 2025Article
- The Statistical Thermodynamics of Generative Diffusion Models: Phase Transitions, Symmetry Breaking, and Critical Instability.Entropy (Basel, Switzerland) · 2025Article
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1 author.
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Abstract
Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Similar to associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work, we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum.
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