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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.07327 |
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| _version_ | 1866911336300019712 |
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| author | Landsmeer, Lennart P. L. Negrello, Mario Hamdioui, Said Strydis, Christos |
| author_facet | Landsmeer, Lennart P. L. Negrello, Mario Hamdioui, Said Strydis, Christos |
| contents | Computational neuroscience relies on large-scale dynamical-systems models of neurons, with a vast amount of offline, pre-simulation, tuned parameters, with models often tied to their brain simulators. These fixed parameters lead to stiff models, that show unnatural behaviour when introduced to new environments, or when combined into larger networks. In contrast to offline tuning, in biology, cells continuously adapt via homeostatic plasticity to stay in desired dynamical regimes. In this work, we aim to introduce such online tuning of cellular parameters into brain simulation. We show that the sensitivity equation of a biorealistic neural models has the same shape as a general neuron model, and can be simulated within existing brain simulators. Via co-simulation with the sensitivity equation, we enable both offline, and online tuning of activity of arbitrary biophysically realistic brain models. Furthermore, we show that this opens the possibility to study the biological mechanisms underlying homeostatic plasticity, via both meta-learning plasticity mechanism as well as treating online tuning as a black-box plasticity mechanism. Through the generality of our methods, we hope that more computational science fields can capitalize on the similarity between the simulated model and its gradient system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gradient Diffusion: Sensitivity-Matrix Co-Simulation Enables Activity Adaptation and Learnable Plasticity in Neural Simulators Landsmeer, Lennart P. L. Negrello, Mario Hamdioui, Said Strydis, Christos Neurons and Cognition Computational neuroscience relies on large-scale dynamical-systems models of neurons, with a vast amount of offline, pre-simulation, tuned parameters, with models often tied to their brain simulators. These fixed parameters lead to stiff models, that show unnatural behaviour when introduced to new environments, or when combined into larger networks. In contrast to offline tuning, in biology, cells continuously adapt via homeostatic plasticity to stay in desired dynamical regimes. In this work, we aim to introduce such online tuning of cellular parameters into brain simulation. We show that the sensitivity equation of a biorealistic neural models has the same shape as a general neuron model, and can be simulated within existing brain simulators. Via co-simulation with the sensitivity equation, we enable both offline, and online tuning of activity of arbitrary biophysically realistic brain models. Furthermore, we show that this opens the possibility to study the biological mechanisms underlying homeostatic plasticity, via both meta-learning plasticity mechanism as well as treating online tuning as a black-box plasticity mechanism. Through the generality of our methods, we hope that more computational science fields can capitalize on the similarity between the simulated model and its gradient system. |
| title | Gradient Diffusion: Sensitivity-Matrix Co-Simulation Enables Activity Adaptation and Learnable Plasticity in Neural Simulators |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2412.07327 |