Cortex-Wide Preservation of Multi-Stimulus Information Across Degrees of Network Synchronization

MCML Authors

Mariia Gladkova

→ Group Daniel Cremers
 Computer Vision & Artificial Intelligence

Steffen Schneider

Dr.

Collaborating PI

Dynamical Inference

Abstract

Sensory-evoked cortical responses vary with global network dynamics, yet the link between cortical state and stimulus processing remains unclear. Here, we introduce a paradigm that jointly decodes stimulus identity and network state from wide-field calcium imaging in mice undergoing multisensory and optogenetic stimulation across isoflurane-induced transitions from compartmentalized to synchronized activity. Effective dimensionality, a summary measure of network complexity, correlated well with anesthesia depth, while non-linear contrastive learning achieved >97% stimulus decoding accuracy across all states. Individual cortical regions maintained ≥82.5% accuracy even during deep anesthesia with prominent slow waves. Preservation of stimulus-specific information extended throughout the cortex, demonstrating that distinct representations remain decodable within synchronized networks. Direct optogenetic cortical stimulation exhibited state-invariant decoding performance, contrasting with anesthesia-dependent decline observed for sensory stimuli. Multi-stimulus cortical representations remain decodable across varying levels of network synchronization, with implications for brain-machine interfaces and clinical tools that assess preserved cortical responsiveness under variable arousal conditions.

misc BGW+25

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Preprint

Dec. 2025

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Authors

S.-V. Bodea • R. G. Laiz • R. H. Williams • M. Gladkova • D. Thalmeier • B. Rieck • F. Sigmund • M. Piraud • A. Wohlschläger • D. Jüstel • A. Stroh • S. Schneider • G. G. Westmeyer

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Links

DOI

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Research Areas

 A3 | Computational Models

 B1 | Computer Vision