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

Paper recorded by Signals 4 on 2026-09-18 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.LG · 机器学习 · first seen 2026-09-21

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

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#1 most recent of 235 cs.LG papers we have recorded · ↓ older: Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confi
Cite this page: BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings: the #1 most recent of 235 cs.LG papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/brainwidebench-benchmarking-large-scale-pretraining-and-across-animal-transfer-i.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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