Dynamical stability for dense patterns in discrete attractor neural networks
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918299195932672 |
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| author | Cohen, Uri Lengyel, Máté |
| author_facet | Cohen, Uri Lengyel, Máté |
| contents | Neural networks storing multiple discrete attractors are canonical models of biological memory. Previously, the dynamical stability of such networks could only be guaranteed under highly restrictive conditions. Here, we derive a theory of the local stability of discrete fixed points in a broad class of networks with graded neural activities and in the presence of noise. By directly analyzing the bulk and the outliers of the Jacobian spectrum, we show that all fixed points are stable below a critical load that is distinct from the classical \textit{critical capacity} and depends on the statistics of neural activities in the fixed points as well as the single-neuron activation function. Our analysis highlights the computational benefits of threshold-linear activation and sparse-like patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamical stability for dense patterns in discrete attractor neural networks Cohen, Uri Lengyel, Máté Disordered Systems and Neural Networks Statistical Mechanics Machine Learning Neural and Evolutionary Computing Neurons and Cognition Neural networks storing multiple discrete attractors are canonical models of biological memory. Previously, the dynamical stability of such networks could only be guaranteed under highly restrictive conditions. Here, we derive a theory of the local stability of discrete fixed points in a broad class of networks with graded neural activities and in the presence of noise. By directly analyzing the bulk and the outliers of the Jacobian spectrum, we show that all fixed points are stable below a critical load that is distinct from the classical \textit{critical capacity} and depends on the statistics of neural activities in the fixed points as well as the single-neuron activation function. Our analysis highlights the computational benefits of threshold-linear activation and sparse-like patterns. |
| title | Dynamical stability for dense patterns in discrete attractor neural networks |
| topic | Disordered Systems and Neural Networks Statistical Mechanics Machine Learning Neural and Evolutionary Computing Neurons and Cognition |
| url | https://arxiv.org/abs/2507.10383 |