Signal denoising through topographic modularity of neural circuits
- Forschungszentrum Jülich, Germany
- Radboud University Nijmegen, Netherlands
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 demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. 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.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code can be found at https://doi.org/10.5281/zenodo.6326496.
Article and author information
Author details
Funding
Initiative and Networking Fund of the Helmholtz Association
- Barna Zajzon
- Abigail Morrison
- Renato Duarte
Helmholtz Portfolio theme Supercomputing and Modeling for the Human Brain
- Barna Zajzon
- Abigail Morrison
- Renato Duarte
Excellence Initiative of the German federal and state governments (G:(DE-82)EXS-SF-neuroIC002)
- Barna Zajzon
- Abigail Morrison
- Renato Duarte
Helmholtz Association (VH-NG-1028)
- David Dahmen
European Commission HBP (945539)
- David Dahmen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Zajzon et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Signal denoising through topographic modularity of neural circuits
eLife 12:e77009.
https://doi.org/10.7554/eLife.77009
