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Updated: Jan 16, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

Parsa Vahidi¹, Omid G Sani¹, Maryam M Shanechi^1,2,3

¹Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA.

|October 3, 2025

Summary

This summary is machine-generated.

We developed BRAID, a deep learning framework that models neural dynamics by incorporating external inputs. This method accurately captures neural-behavioral relationships and improves forecasting by disentangling intrinsic dynamics from input effects.

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Area of Science:

Computational Neuroscience
Machine Learning
Systems Neuroscience

Background:

Neural populations exhibit complex dynamics influenced by external inputs.
Traditional models often overlook the impact of these inputs on neural activity and behavior.
Understanding intrinsic neural dynamics is crucial for explaining behavior.

Purpose of the Study:

Introduce BRAID, a deep learning framework to model nonlinear neural dynamics.
Explicitly incorporate external inputs into neural population models.
Disentangle intrinsic neural dynamics from input effects to improve behavioral prediction.

Main Methods:

Developed BRAID, a deep learning framework using input-driven recurrent neural networks.
Incorporated a forecasting objective to disentangle dynamics from inputs.
Utilized a multi-stage optimization scheme to prioritize behavior-related intrinsic dynamics.
Validated with nonlinear simulations and applied to motor cortical activity data.

Main Results:

BRAID accurately learns intrinsic dynamics shared between neural and behavioral data in simulations.
Applying BRAID to motor cortical activity improved data fitting by incorporating sensory stimuli.
The framework enhanced forecasting of neural-behavioral data compared to baseline methods.

Conclusions:

BRAID offers a novel approach to modeling neural dynamics by integrating external inputs.
The method effectively disentangles intrinsic dynamics from input influences.
BRAID improves the understanding and prediction of neural activity and behavior.