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Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace.

Oren Weiss¹, Ruben Coen-Cagli²

¹Department of Systems and Computational Biology and Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA oren.weiss@einsteinmed.edu.

Neural Computation

|July 24, 2025

Summary

This summary is machine-generated.

Neural response variability impacts sensory information transmission between brain areas. This study introduces a framework to analyze how variability affects information flow, aiding understanding of neural communication.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Sensory information processing relies on neural communication across brain regions.
Neural population response variability can limit stimulus information representation.
The effect of this variability on interareal communication remains unclear.

Purpose of the Study:

To develop a mathematical framework for understanding how neural population response variability impacts sensory information transmission.
To investigate the role of communication subspaces in mediating interareal information flow.
To provide a theoretical basis for analyzing and potentially manipulating sensory information routing in the brain.

Main Methods:

Combined linear Fisher information with the communication subspace framework.
Partitioned Fisher information based on the alignment of population covariance and mean tuning direction.
Utilized mathematical and numerical analyses to examine theoretical scenarios.

Main Results:

Developed a method to decompose Fisher information, separating contributions related to the communication subspace and its orthogonal complement.
Demonstrated how population variability, when aligned with communication subspaces, can influence information transmission.
Identified theoretical mechanisms for flexible routing and gating of sensory information.

Conclusions:

The proposed framework offers a novel perspective on how neural variability shapes interareal communication of sensory information.
Understanding this relationship is crucial for comprehending neural coding and information flow in complex sensory systems.
This work provides a theoretical foundation to guide future experimental investigations into neural communication and information processing.