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Signal denoising through topographic modularity of neural circuits

View ORCID ProfileBarna Zajzon, View ORCID ProfileDavid Dahmen, View ORCID ProfileAbigail Morrison, View ORCID ProfileRenato Duarte
doi: https://doi.org/10.1101/2022.01.10.475681
Barna Zajzon
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
2Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
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  • For correspondence: b.zajzon@fz-juelich.de
David Dahmen
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
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Abigail Morrison
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
3Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
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Renato Duarte
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
4Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
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Abstract

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

Competing Interest Statement

The authors have declared no competing interest.

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 January 12, 2022.
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Signal denoising through topographic modularity of neural circuits
Barna Zajzon, David Dahmen, Abigail Morrison, Renato Duarte
bioRxiv 2022.01.10.475681; doi: https://doi.org/10.1101/2022.01.10.475681
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Signal denoising through topographic modularity of neural circuits
Barna Zajzon, David Dahmen, Abigail Morrison, Renato Duarte
bioRxiv 2022.01.10.475681; doi: https://doi.org/10.1101/2022.01.10.475681

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