Manifolds for the neural representation of 3D spatial experience

The precision with which animals navigate and interact with objects depends in part on how their brains represent position and orientation in three dimensions. A central challenge is to explain within a common framework how neural systems ranging from insects to primates represent three-dimensional (3D) pose. Here we develop a framework based on the hypothesis that neural representations of pose lie on manifolds derived from the Special Euclidean Group, SE(3), which describes the full space of rigid-body positions and orientations in three dimensions. The framework shows how movement constraints, object geometry, and visual features jointly determine the topology of the resulting pose manifold, yielding a taxonomy that guides experimental tests. We apply the framework to derive low-dimensional manifolds for neural representations of 3D object orientation in macaque parietal cortex and head direction in the limbic systems of fruit bats, mice, and monkeys. This analysis also guides the construction of neuronal tuning functions that respect the topology determined by experimental conditions. Together, these results provide a common approach for linking the geometry of 3D pose to neural coding across species and brain systems.

Authors

Institutions

Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-27
DOI
https://doi.org/10.64898/2026.10.22.760836
Primary Topic
Memory and Neural Mechanisms
Type
preprint

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Manifolds for the neural representation of 3D spatial experience

Ari Rosenberg, Raymond C. Doudlah, ByoungHoon Kim, Noah John Cowan et al.
bioRxiv (Cold Spring Harbor Laboratory)
Memory and Neural Mechanisms
preprint

Manifolds for the neural representation of 3D spatial experience

Ari Rosenberg, Raymond C. Doudlah, ByoungHoon Kim, Noah John Cowan, Satchal Postlewaite, Mengyu Xiao
preprint en

Abstract

The precision with which animals navigate and interact with objects depends in part on how their brains represent position and orientation in three dimensions. A central challenge is to explain within a common framework how neural systems ranging from insects to primates represent three-dimensional (3D) pose. Here we develop a framework based on the hypothesis that neural representations of pose lie on manifolds derived from the Special Euclidean Group, SE(3), which describes the full space of rigid-body positions and orientations in three dimensions. The framework shows how movement constraints, object geometry, and visual features jointly determine the topology of the resulting pose manifold, yielding a taxonomy that guides experimental tests. We apply the framework to derive low-dimensional manifolds for neural representations of 3D object orientation in macaque parietal cortex and head direction in the limbic systems of fruit bats, mice, and monkeys. This analysis also guides the construction of neuronal tuning functions that respect the topology determined by experimental conditions. Together, these results provide a common approach for linking the geometry of 3D pose to neural coding across species and brain systems.

bioRxiv (Cold Spring Harbor Laboratory)
University of Wisconsin–Madison (US), Johns Hopkins University (US), University of Chicago (US)
Air Force Office of Scientific Research
Memory and Neural Mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Manifolds for the neural representation of 3D spatial experience — Ari Rosenberg, Raymond C. Doudlah, et al. · bioRxiv (Cold Spring Harbor Laboratory) (2026) | TGRS Research Map | TGRS