ArticleNature neuroscience2022
The role of population structure in computations through neural dynamics.
Article in Nature neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.
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115 citing papers in PubMed.
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- Synaptic-state-dependent plasticity routes learning signals in recurrent circuits.bioRxiv : the preprint server for biology · 2026Article
- Sensorimotor remapping drives task specialization in prefrontal cortex.Nature communications · 2026Article
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
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- Balancing Stability and Flow in Hippocampal Networks via Inductive Bias and Learned Symmetry Breaking.bioRxiv : the preprint server for biology · 2026Article
- Predictive pursuit emerges in high-dimensional recurrent neural networks.bioRxiv : the preprint server for biology · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
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- Sensory integration, temporal prediction, and rule discovery reflect interdependent inference processes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
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- Design principles for cost functions in optimal sensory coding.Frontiers in computational neuroscience · 2026Review
- Structured interconnectivity optimizes neural geometry for balancing specificity and generalization in object recognition.Communications biology · 2025Article
- Neurons in auditory cortex integrate information within a constrained and context-invariant temporal window.Current biology : CB · 2025Article
55 more citing papers are in PubMed but not listed here.
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5 authors.
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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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