Hierarchy of chaotic dynamics in random modular networks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912354110799872 |
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| author | Kuśmierz, Łukasz Pereira-Obilinovic, Ulises Lu, Zhixin Mastrovito, Dana Mihalas, Stefan |
| author_facet | Kuśmierz, Łukasz Pereira-Obilinovic, Ulises Lu, Zhixin Mastrovito, Dana Mihalas, Stefan |
| contents | We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high values of the Lyapunov dimension that change significantly across the region. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity. Extending the model to include a multilevel, hierarchical connectivity reveals that a loose balance between activities across levels drives the system towards the edge of chaos. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06361 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchy of chaotic dynamics in random modular networks Kuśmierz, Łukasz Pereira-Obilinovic, Ulises Lu, Zhixin Mastrovito, Dana Mihalas, Stefan Biological Physics Disordered Systems and Neural Networks Neural and Evolutionary Computing Chaotic Dynamics Neurons and Cognition We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high values of the Lyapunov dimension that change significantly across the region. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity. Extending the model to include a multilevel, hierarchical connectivity reveals that a loose balance between activities across levels drives the system towards the edge of chaos. |
| title | Hierarchy of chaotic dynamics in random modular networks |
| topic | Biological Physics Disordered Systems and Neural Networks Neural and Evolutionary Computing Chaotic Dynamics Neurons and Cognition |
| url | https://arxiv.org/abs/2410.06361 |