The role of population structure in computations through neural dynamics
| Autor: | Adrian Valente, Alexis Dubreuil, Francesca Mastrogiuseppe, Manuel Beiran, Srdjan Ostojic |
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| Přispěvatelé: | Ostojic, Srdjan, Université de Bordeaux (UB), Laboratoire de Neurosciences Cognitives & Computationnelles (LNC2), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Columbia University [New York], Champalimaud Centre for the Unknown [Lisbon] |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Neurons
Theoretical computer science Computer science General Neuroscience Computation Population structure Models Neurological Sorting Structure (category theory) Mechanism based Article Dynamics (music) [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] Random population Curse of dimensionality |
| Zdroj: | Nature Neuroscience Nature Neuroscience, Nature Publishing Group, 2022, 25 (6), pp.783-794. ⟨10.1038/s41593-022-01088-4⟩ Nat Neurosci |
| ISSN: | 1097-6256 1546-1726 |
| DOI: | 10.1038/s41593-022-01088-4⟩ |
| Popis: | Neural computations are currently investigated using two separate approaches: sorting neurons into functional populations, or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and cell-class structure play fundamentally complementary roles. While various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input-output mappings instead required a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a population structure enabled flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the dynamical landscape of collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, inactivation experiments, and for the implication of different neurons in multi-tasking. |
| Databáze: | OpenAIRE |
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