Characterizing interactions between brain areas is a fundamental goal of systems neuroscience. While such analyses are possible when areas are recorded simultaneously, it is rare to observe all combinations of areas of interest within a single animal or recording session. How can we leverage multi-animal datasets to better understand multi-area interactions? Building on recent progress in large-scale, multi-animal models, we introduce NeuroPaint, a masked autoencoding approach for inferring the dynamics of unrecorded brain areas. By training across animals with overlapping subsets of recorded areas, NeuroPaint learns to reconstruct activity in missing areas based on shared structure across individuals. We train and evaluate our approach on synthetic data and two multi-animal, multi-area Neuropixels datasets. Our results demonstrate that models trained across animals with partial observations can successfully in-paint the dynamics of unrecorded areas, enabling 39th Conference on Neural Information Processing Systems (NeurIPS 2025).

We evaluate Okapi using three datasets - iWildCam, PovertyMap, and CivilComments - taken from the WILDS 2.0 benchmark [63]. These datasets were chosen specifically due to the poor performance reported by [63] for semi-supervised and domain adaptation methods across the board, in relation to the ERM baselines. For PovertyMap in particular, ERM was found to vastly outperform any competing methods utilising the unlabelled data and/or domain labels. The task is multiclass species classification of animals in camera trap images. The dataset contains 1022K images of animals annotated with the domain, s, that identifies the camera trap that captured it.

Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.

Neural Information Processing Systems http://nips.cc/