BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings

📅 2026-09-18
📈 Citations: 0
Influential: 0
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本文提出BrainWideBench,通过大规模预训练和跨动物迁移来解决多区域神经记录中的通用表示学习问题。
📝 Abstract
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising the question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream tasks. Yet, progress toward this goal has been limited by fragmented evaluation protocols and a narrow focus on individual task domains. Here, we present BrainWideBench, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task. The benchmark is organized around three complementary task suites that evaluate whether learned representations support downstream decoding of behavior, can predict masked or future neural activity, and can recover biologically meaningful anatomical organization. With this benchmark, we systematically evaluate pretraining methods across transfer settings, including finetuning on downstream objectives and zero-shot generalization to unseen animals. Our results confirm pretraining improves performance over matched single-session baselines, but we show current methods exhibit heterogeneity in transfer capabilities: gains depend strongly on the alignment between pretraining objectives and downstream tasks. No single approach performs uniformly well across all three suites, and most methods are designed to only address a subset of them. Together, these findings suggest that learning representations that jointly generalize across behavior, dynamics, and anatomy remains an open challenge. By providing a unified and reproducible evaluation suite, BrainWideBench establishes a framework for measuring progress toward general-purpose models of the mouse brain.
Problem

Research questions and friction points this paper is trying to address.

large-scale neural recording
general-purpose neural representations
across-animal transfer
downstream tasks
evaluation protocols
Innovation

Methods, ideas, or system contributions that make the work stand out.

BrainWideBench
large-scale pretraining
across-animal transfer
multi-region neural recordings
general-purpose neural representations
A
Alexandre Andre
University of Pennsylvania
S
Shivashriganesh P. Mahato
University of Pennsylvania
V
Vinam Arora
University of Pennsylvania
K
Keshav Balaji
University of Pennsylvania
Divyansha Lachi
Divyansha Lachi
Graduate Student, University of Pennsylvania
Geometric Deep LearningReinforcement LearningComputational Neuroscience
N
Nanda H. Krishna
Mila, Université de Montréal
Jingyun Xiao
Jingyun Xiao
Georgia Institute of Technology
Computer VisionDeep LearningComputational NeuroscienceBrain-computer Interface
Y
Yizi Zhang
Stanford University
Ximeng Mao
Ximeng Mao
Mila, University of Montreal
machine learningdeep learningrepresentational learning
W
Wenrui Ma
University of Pennsylvania
H
Han Yu
Columbia University
International Brain Laboratory
International Brain Laboratory
www.internationalbrainlab.org
Systems NeuroscienceComputational Neuroscience
D
Daniel Birman
Allen Institute
N
Niccolò Bonacchi
William James Center for Research, ISPA - Instituto Universitário
G
Gaelle A. Chapuis
University of Geneva
J
Joana A. Catarino
Karolinska Institutet
F
Felicia Davatolhagh
UCLA
M
Mayo Faulkner
University College London
L
Laura Freitas-Silva
Champalimaud Foundation
F
Fei Hu
Lingang Laboratory
J
Julia M. Huntenburg
Champalimaud Foundation
Anup Khanal
Anup Khanal
UCLA
I
Inês Laranjeira
Champalimaud Foundation
P
Petrina Lau
The Chinese University of Hong Kong
G
Guido T. Meijer
Donders Institute