Complementary roles of dimensionality and population structure in neural computations
By Alexis M. Dubreuil, Adrian Valente, Manuel Beirán, Francesca Mastrogiuseppe, Srdjan Ostojic. 2020
40 citations recorded by the source corpus.
Neural computations are currently investigated using two competing approaches: sorting neurons into functional classes, 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 with machine-learning tools 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 consisting of a single global population, flexible input-output mappings instead required networks to be organized into several sub-populations. Our analyses revealed that the subpopulation 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 and inactivation experiments.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering
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Citations in the corpus
- Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “However, how the neural representations that support context-dependent task performance are acquired remains a key open question for cognitive and neural scientists (Gao and Ganguli, 2015; Mastrogiuseppe andOstojic, 2018; Cueva et al., 2020; Dubreuil et al., 2020; Badre et al., 2021; Freund et al., ”
- Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021, methodology): “A second example of model reduction is mean-field analysis, that can in particular be used to extract the dynamics of latent variables directly from trained RNNs [92].”