Linear Readout of Neural Manifolds with Continuous Variables

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Slatton, Will, Chou, Chi-Ning, Chung, SueYeon
Format: Preprint
Published: 2026
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866908879444508672
author Slatton, Will
Chou, Chi-Ning
Chung, SueYeon
author_facet Slatton, Will
Chou, Chi-Ning
Chung, SueYeon
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_10956
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Linear Readout of Neural Manifolds with Continuous Variables
Slatton, Will
Chou, Chi-Ning
Chung, SueYeon
Neurons and Cognition
Disordered Systems and Neural Networks
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.
title Linear Readout of Neural Manifolds with Continuous Variables
topic Neurons and Cognition
Disordered Systems and Neural Networks
url https://arxiv.org/abs/2603.10956