Signatures of hierarchical temporal processing in the mouse visual system

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-08-22
DOI
https://doi.org/10.1371/journal.pcbi.1012355
Citations
9
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
3.08

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article

Signatures of hierarchical temporal processing in the mouse visual system

Benjamin Cramer, F. Paul Spitzner, D. Marx, Viola Priesemann et al.
9 citations
PLoS Computational Biology
Neural dynamics and brain function
3.08
article

Signatures of hierarchical temporal processing in the mouse visual system

Benjamin Cramer, F. Paul Spitzner, D. Marx, Viola Priesemann, Lucas Rudelt, Johannes Zierenberg
article en
9 citations

Abstract

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.

PLoS Computational BiologyVol. 20(8)
Heidelberg University (DE), Bernstein Center for Computational Neuroscience Göttingen (DE), Max Planck Institute for Dynamics and Self-Organization (DE), Kirchhoff (Germany) (DE), University of Göttingen (DE)
Deutsche Forschungsgemeinschaft, Max-Planck-Gesellschaft
Openalex Percentile: Top 8%
Neural dynamics and brain function
3.08
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Signatures of hierarchical temporal processing in the mouse visual system — Benjamin Cramer, F. Paul Spitzner, et al. · PLoS Computational Biology (2024) | TGRS Research Map | TGRS