This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.7554/eLife.77009 in citations.
Please use the identifier: http://hdl.handle.net/2128/34377 in citations.
Signal denoising through topographic modularity of neural circuits
Signal denoising through topographic modularity of neural circuits
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, th...
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Personal Name(s): | Zajzon, Barna (Corresponding author) |
---|---|
Dahmen, David / Morrison, Abigail / Duarte, Renato | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | eLife, 12 (2023) S. e77009 |
Imprint: |
Cambridge
eLife Sciences Publications
2023
|
DOI: |
10.7554/eLife.77009 |
Document Type: |
Journal Article |
Research Program: |
Human Brain Project Specific Grant Agreement 3 Recurrence and stochasticity for neuro-inspired computation Supercomputing and Modelling for the Human Brain Computational Principles |
Link: |
Get full text OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/34377 in citations.
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. |