Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace

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.