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MARBLE: interpretable representations of neural population dynamics using geometric deep learning.

Adam Gosztolai¹, Robert L Peach^2,3, Alexis Arnaudon⁴

¹Institute of Artificial Intelligence, Medical University of Vienna, Vienna, Austria. adam.gosztolai@meduniwien.ac.at.

|February 17, 2025

Summary

This summary is machine-generated.

We developed MARBLE, a novel representation learning method for neural dynamics. MARBLE uncovers consistent low-dimensional latent representations of complex neural activity across different systems and tasks.

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Area of Science:

Computational Neuroscience
Machine Learning
Dynamical Systems

Background:

Neural population dynamics often occur on low-dimensional manifolds.
Learning these dynamics is crucial for interpretable latent representations.

Purpose of the Study:

Introduce MARBLE (Manifold Representation learning via Backward-flow LEarning), a novel method for learning neural dynamics.
Infer interpretable and consistent latent representations from high-dimensional neural data.

Main Methods:

MARBLE decomposes on-manifold dynamics into local flow fields.
It uses unsupervised geometric deep learning to map dynamics into a common latent space.

Main Results:

Discovered emergent low-dimensional latent representations in simulated systems, RNNs, and primate/rodent neural recordings.
These representations parametrize neural dynamics during various cognitive tasks.
Achieved state-of-the-art decoding accuracy across and within animals.

Conclusions:

Manifold structure offers a powerful inductive bias for decoding algorithms.
MARBLE enables robust comparison of cognitive computations across diverse neural systems.
The method requires minimal user input for effective data assimilation.