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Strong coupling and local control of dimensionality across brain areas

View ORCID ProfileDavid Dahmen, View ORCID ProfileStefano Recanatesi, View ORCID ProfileGabriel K. Ocker, View ORCID ProfileXiaoxuan Jia, View ORCID ProfileMoritz Helias, View ORCID ProfileEric Shea-Brown
doi: https://doi.org/10.1101/2020.11.02.365072
David Dahmen
1Institute of Neuroscience and Medicine (INM-6 and INM-10) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
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  • For correspondence: d.dahmen@fz-juelich.de
Stefano Recanatesi
2University of Washington Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, Seattle, WA, USA
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Gabriel K. Ocker
3Allen Institute, Seattle, WA, USA
4Department of Mathematics and Statistics, Boston University, MA, USA
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Xiaoxuan Jia
3Allen Institute, Seattle, WA, USA
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Moritz Helias
1Institute of Neuroscience and Medicine (INM-6 and INM-10) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
5Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
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Eric Shea-Brown
2University of Washington Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, Seattle, WA, USA
3Allen Institute, Seattle, WA, USA
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Abstract

The dimensionality of a network’s collective activity is the number of modes into which it is organized. This quantity is of great interest in neural coding: small dimensionality suggests a compressed neural code and possibly high robustness and generalizability, while high dimensionality suggests expansion of input features to enable flexible downstream computation. Here, for recurrent neural circuits operating in the ubiquitous balanced regime, we show how dimensionality arises mechanistically via perhaps the most basic property of neural circuits: a single number characterizing the net strength of their connectivity. Our results combine novel theoretical approaches with new analyses of high-density neuropixels recordings and high-throughput synaptic physiology datasets. The analysis of electrophysiological recordings identifies bounds on the dimensionality of neural responses across brain regions, showing that it is on the order of hundreds – striking a balance between high and low-dimensional codes. Furthermore, focusing on the visual stream, we show that dimensionality expands from primary to deeper visual areas and similarly within an area from layer 2/3 to layer 5. We interpret these results via a novel theoretical result which links dimensionality to a single measure of net connectivity strength. This requires calculations that extend beyond traditional mean-field approaches to neural networks. Our result suggests that areas across the brain operate in a strongly coupled regime where dimensionality is under sensitive control by net connectivity strength; moreover, we show how this net connectivity strength is regulated by local connectivity features, or synaptic motifs. This enables us to interpret changes in dimensionality in terms of changes in coupling among pairs and triplets of neurons. Analysis of large-scale synaptic physiology datasets from both mouse and human cortex then reveal the presence of synaptic coupling motifs capable of substantially regulating this dimensionality.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* These authors share first authorship

  • ↵+ These authors share senior authorship

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 04, 2020.
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Strong coupling and local control of dimensionality across brain areas
David Dahmen, Stefano Recanatesi, Gabriel K. Ocker, Xiaoxuan Jia, Moritz Helias, Eric Shea-Brown
bioRxiv 2020.11.02.365072; doi: https://doi.org/10.1101/2020.11.02.365072
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Strong coupling and local control of dimensionality across brain areas
David Dahmen, Stefano Recanatesi, Gabriel K. Ocker, Xiaoxuan Jia, Moritz Helias, Eric Shea-Brown
bioRxiv 2020.11.02.365072; doi: https://doi.org/10.1101/2020.11.02.365072

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