A shared manifold for scalable temporal representations in self-paced timing
- 1University of Oxford
Abstract
Adaptive behaviour often relies on tracking the passage of time, yet how distinct brain regions generate coherent temporal representations remains unclear. Using a self-paced interval timing task in mice, we show that heterogeneous single-neuron temporal firing profiles distributed across regions are organized within a shared ring manifold. Within this low-dimensional space, population activity evolves along a common trajectory across different intervals and encodes elapsed time in a relative reference frame. Different durations are not represented by separate neural states, but by modulation of traversal speed. These scalable dynamics arise from coordinated population-wide co-scaling of single-neuron activity and support trial-by-trial adjustments in timing behaviour. A cross-regional assembly of start neurons predicts, at interval onset, upcoming waiting duration and behavioural adjustments, linking initial population states to trajectory evolution. Together, these findings identify population traversal of a shared activity manifold as a mechanism for scalable temporal representation across brain regions.
Related
- TractSpLearn: Specialized Shared-Manifold Learning for Individualized Detection of Subtle White Matter Alterations in Mild Traumatic Brain Injury
- Shared neural geometries for bilingual semantic representations in human hippocampal neurons
- Is Oxidative Stress a Shared Mechanism Linking Autism Regression, Late Diagnosis, and Sex-Disparate Diagnostic Timing?
- Temporal compression of gonadal growth preserves reproductive timing and soma-to-germline scaling under stress
- Source-Decomposed Multimodal Graph Connectivity: A Temporal Mixture-Manifold Pipeline for Tracking Ischemic Injury and Treatment
BibTeX
@misc{serrano2026shared,
title = {{A shared manifold for scalable temporal representations in self-paced timing}},
author = {M. Serrano and M. Castelli and Y. Peng and A. Sharott and D. Dupret},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.03.749079},
url = {https://www.biorxiv.org/content/10.64898/2026.09.03.749079v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A shared manifold for scalable temporal representations in self-paced timing
AU - M. Serrano
AU - M. Castelli
AU - Y. Peng
AU - A. Sharott
AU - D. Dupret
PY - 2026
DA - 2026/09/03
PB - bioRxiv
DO - 10.64898/2026.09.03.749079
UR - https://www.biorxiv.org/content/10.64898/2026.09.03.749079v1
AB - Adaptive behaviour often relies on tracking the passage of time, yet how distinct brain regions generate coherent temporal representations remains unclear. Using a self-paced interval timing task in mice, we show that heterogeneous single-neuron temporal firing profiles distributed across regions are organized within a shared ring manifold. Within this low-dimensional space, population activity evolves along a common trajectory across different intervals and encodes elapsed time in a relative reference frame. Different durations are not represented by separate neural states, but by modulation of traversal speed. These scalable dynamics arise from coordinated population-wide co-scaling of single-neuron activity and support trial-by-trial adjustments in timing behaviour. A cross-regional assembly of start neurons predicts, at interval onset, upcoming waiting duration and behavioural adjustments, linking initial population states to trajectory evolution. Together, these findings identify population traversal of a shared activity manifold as a mechanism for scalable temporal representation across brain regions.
ER -