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Association Areas of the Cortex

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Related Experiment Video

Updated: Jul 10, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

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Published on: December 5, 2014

Facemap: a framework for modeling neural activity based on orofacial tracking.

Atika Syeda¹, Lin Zhong², Renee Tung²

¹HHMI Janelia Research Campus, Ashburn, VA, USA. syedaa@janelia.hhmi.org.

Nature Neuroscience

|November 21, 2023

Summary

This summary is machine-generated.

Facemap accurately tracks mouse orofacial behaviors, enabling better computational models to link neural activity with behavior. This framework significantly advances understanding of brain-wide neural signals.

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Last Updated: Jul 10, 2025

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Area of Science:

Neuroscience
Computational Biology
Behavioral Science

Background:

Orofacial behaviors in mice generate substantial neural activity across the brain.
Understanding the relationship between behavior and neural signals requires advanced computational models.

Purpose of the Study:

To develop a computational framework for accurately tracking orofacial behaviors and predicting neural activity.
To improve the characterization of behavior-neural signal dynamics.

Main Methods:

Development of Facemap, a framework combining a keypoint tracker and a deep neural network encoder.
High-accuracy, high-speed tracking of mouse orofacial behaviors.
Deep neural network model for predicting neural activity from behavioral keypoints.

Main Results:

Facemap demonstrated superior accuracy and speed compared to existing pose estimation tools.
The model achieved near-optimal performance with minimal annotated frames, showing adaptability.
Facemap doubled the explained variance in visual cortex neural activity compared to previous methods.
Predicted neuronal activity clusters were spatially widespread, and behavioral features exhibited unidirectional sequential dynamics.

Conclusions:

Facemap offers a powerful and adaptable tool for real-time behavioral analysis and neural activity prediction.
The framework facilitates deeper insights into the function of brain-wide neural signals and their behavioral correlates.