The role of population structure in computations through neural dynamics
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Abstract
Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations 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 subpopulation structure play fundamentally complementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input-output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.
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When RNN architecture is explicitly designed to include dedicated neural subpopulations, these subpopulations can improve model performance on particular types of tasks.
“When RNN architecture is explicitly designed to include dedicated neural subpopulations, these subpopulations can improve model performance on particular types of tasks (Beiran et al., 2021; Dubreuil et al., 2022)”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Dubreuil et al., 2022
When RNN architecture is explicitly designed to include dedicated neural subpopulations, these subpopulations can improve model performance on particular types of tasks.
“When RNN architecture is explicitly designed to include dedicated neural subpopulations, these subpopulations can improve model performance on particular types of tasks (Beiran et al., 2021; Dubreuil et al., 2022).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Dubreuil et al., 2022
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- Authors
- Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F., & Ostojic, S.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Dubreuil, Alexis
- Valente, Adrian
- Ostojic, Srdjan
- Venue
- Nature Neuroscience
- Published
- 2022-06
- Volume
- 25
- Issue
- 6
- Pages
- 783-794
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC9284159
- OpenAlex
- W4281663412
- Cited by
- 237
- References
- 73
- Citations by year
- 2026: 47 · 2025: 64 · 2024: 53 · 2023: 42 · 2022: 24 · 2021: 6 · 2020: 1
- FWCI
- 23.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- SDL (submittedVersion, cc-by-nc-nd)
- Funders
- Agence Nationale de la Recherche (CRCNS PIND, ANR-17-EURE-0017, ANR-16-CE37-0016)
- U.S. Department of Health & Human Services | National Institutes of Health (U01 NS122123)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- gain modulation, recurrent neural networks, neural computation, input-output mapping, neuronal selectivity, multitasking, cognitive tasks
- MeSH terms
- Models, Neurological, Neurons
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