---
title: "Shared Representation Discovery (ShaReD) for Multi-Subject Neural Data Analysis"
id: "biorxiv-11-shared-representation-discovery-for-multi-subject-neural-data-analysis"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-11-shared-representation-discovery-for-multi-subject-neural-data-analysis"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1?rss=1"
published_at: "2026-08-23T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Shared Representation Discovery (ShaReD) for Multi-Subject Neural Data Analysis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-11-shared-representation-discovery-for-multi-subject-neural-data-analysis
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1?rss=1)
- **Published At:** 2026-08-23T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Neural recordings vary across individuals even when behavior is similar because sampled neurons differ and behavior timing is not aligned. Standard cross-subject methods require temporal or anatomical correspondence and thus exclude many datasets. - The authors introduce **Shared Representation Discovery (ShaReD)**, a method that learns a **shared behavioral projection** concurrently with subject-specific neural projections to identify neural–behavioral relationships conserved across subjects. - ShaReD was developed and benchmarked using three dataset types: synthetic data, non-human primate motor cortex recordings, and rat recordings from a spatial alternation task. - On synthetic data, ShaReD recovered common underlying structure across varied noise levels, sample sizes, and numbers of subjects, and it separated components confined to different subject groups (details of metrics not reported in the source). - In primate motor cortex data, ShaReD identified kinematic representations that generalized across individual animals and across reaching tasks with differing movement statistics (specific tasks and statistical measures not reported in the source). - In rat data, ShaReD isolated behavior-aligned directions within a communication subspace spanning CA1 to prefrontal cortex (PFC), indicating conserved behavior-related components across subjects in this circuit. - The method therefore extends multi-subject neural data analysis to datasets in which comparable behaviors occur without cross-subject temporal correspondence, enabling discovery of conserved neural–behavioral mappings when standard alignment assumptions fail. - Funding for the work included support from the National Institute of Neurological Disorders and Stroke (R01NS125298). The authors declared no competing interests.
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Felix H Taschbach 1 University of California San Diego; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Felix%2BH%2BTaschbach%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Taschbach%20FH&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AFelix%2BH%2BTaschbach%2B) * [ORCID record for Felix H Taschbach](http://orcid.org/0000-0001-5345-5787 "Open in new tab") Marcus K Benna 2 UC San Diego: University of California San Diego * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Marcus%2BK%2BBenna%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Benna%20MK&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMarcus%2BK%2BBenna%2B) * For correspondence: mbenna@ucsd.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5724083/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5724083/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5724083/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5724083/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.08.18.745594v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5724083/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Neural recordings from different individuals vary substantially even when behavior is broadly shared. The sampled neurons differ, and the same behavior occurs at different times. Standard cross-subject analyses rely on matched time points or anatomical correspondence, which excludes many datasets. We recently introduced Shared Representation Discovery (ShaReD), which identifies neural-behavioral relationships conserved across subjects by learning a shared behavioral projection together with subject-specific neural projections. Here we develop and benchmark this method using synthetic, primate, and rat data. On synthetic data, ShaReD recovers common structure across noise levels, sample sizes, and subject counts, and separates components confined to different groups of subjects. In non-human primate motor cortex, ShaReD identifies kinematic representations that generalize across individuals and across reaching tasks with different movement statistics. In rats navigating a spatial alternation task, ShaReD isolates behavior-aligned directions within the CA1-to-PFC communication subspace. ShaReD thus extends multi-subject analysis to datasets in which comparable behaviors occur without cross-subject temporal correspondence. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared National Institute of Neurological Disorders and Stroke, https://ror.org/01s5ya894, R01NS125298 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC 4.0 International license](http://creativecommons.org/licenses/by-nc/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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