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Updated: Jan 10, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.

Ji Xia¹, Yizi Zhang², Shuqi Wang³

¹Center for Theoretical Neuroscience, Zuckerman Mind Brain Behavior Institute, Kavli Institute for Brain Science, Columbia University.

|November 24, 2025

Summary

This summary is machine-generated.

NeuroPaint infers brain area dynamics from incomplete data by training across multiple animals. This approach reconstructs unrecorded neural activity, enabling comprehensive multi-area interaction studies.

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Area of Science:

Systems neuroscience
Computational neuroscience
Neuroimaging analysis

Background:

Understanding brain area interactions is key in systems neuroscience.
Simultaneous recording of all brain areas is often infeasible within a single experiment.
Multi-animal datasets offer potential for studying broader neural interactions.

Purpose of the Study:

To develop a method for inferring the dynamics of unrecorded brain areas using multi-animal data.
To enable multi-area interaction analyses despite limitations of individual recordings.
To leverage masked autoencoding for reconstructing neural activity patterns.

Main Methods:

Introduced NeuroPaint, a masked autoencoding model.
Trained the model across multiple animals with overlapping recorded brain areas.
Evaluated performance on synthetic and real-world Neuropixels datasets.

Main Results:

NeuroPaint successfully reconstructs the dynamics of unrecorded brain areas.
The model leverages shared structure across individuals with partial observations.
Enables multi-area analyses beyond the scope of single experiments.

Conclusions:

Masked autoencoding across animals is effective for inferring neural dynamics.
NeuroPaint overcomes limitations of incomplete neural recordings.
Facilitates more holistic understanding of brain-wide interactions.