Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910173358981120 |
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| author | Vivet, Arnau Arenas, Alex |
| author_facet | Vivet, Arnau Arenas, Alex |
| contents | We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an entropy-regularized replicator equation. The coupled neuron-astrocyte dynamics admit a Lyapunov function, ensuring global convergence. At fixed points, astrocytic gains implement a softmax-normalized allocation over pattern similarity scores, yielding a mechanistic realization of self-attention as emergent routing on the gain simplex. In regimes of high memory load and interference, the model significantly improves retrieval accuracy relative to classical Hopfield dynamics and recent neuron-astrocyte baselines. These results establish a dynamical systems framework linking glial modulation, competitive resource allocation, and attention-like computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics Vivet, Arnau Arenas, Alex Data Analysis, Statistics and Probability Machine Learning Adaptation and Self-Organizing Systems Physics and Society We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an entropy-regularized replicator equation. The coupled neuron-astrocyte dynamics admit a Lyapunov function, ensuring global convergence. At fixed points, astrocytic gains implement a softmax-normalized allocation over pattern similarity scores, yielding a mechanistic realization of self-attention as emergent routing on the gain simplex. In regimes of high memory load and interference, the model significantly improves retrieval accuracy relative to classical Hopfield dynamics and recent neuron-astrocyte baselines. These results establish a dynamical systems framework linking glial modulation, competitive resource allocation, and attention-like computation. |
| title | Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics |
| topic | Data Analysis, Statistics and Probability Machine Learning Adaptation and Self-Organizing Systems Physics and Society |
| url | https://arxiv.org/abs/2604.25481 |