Work

Linear Readout of Neural Manifolds with Continuous Variables

Year: 2026

Type: preprint

Abstract: Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability in neural responses makes it difficult to link internal representational structure to task performance. We develop a statistical-mechanical theory of regression capacity that relates linear decoding efficiency of continuous variables to geometric properties of neural manifolds. Our theory handles complex neural variability and applies to real data, revealing increasing capacity for decoding object position and size along the monkey visual stream.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.10956

Authors: Slatton, Will, Chou, Chi-Ning, Chung, SueYeon

Institutions: Harvard University Press, Flatiron Health (United States)

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Simons Foundation, Harvard University, Samsung Advanced Institute of Technology, Flatiron Health, National Institutes of Health, Samsung

Award: A multi-level framework for characterizing task-efficient coding geometry of neural population activities

OpenAlex ID: W7135039516

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