Flow-field inference from neural data using deep recurrent networks
Timothy Doyeon Kim, Thomas Zhihao Luo, Tankut Can, Kamesh Krishnamurthy, Jonathan W. Pillow, Carlos D Brody
Abstract
Neural computations underlying processes such as decision-making, working memory, and motor control are thought to emerge from neural population dynamics. But estimating these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method for inferring low-dimensional, nonlinear, stochastic dynamics underlying neural population activity. Using spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR performs competitively with existing methods in capturing the heterogeneous responses of individual neurons. When trained to disentangle task-relevant and irrelevant activity, FINDR uncovers interpretable low-dimensional dynamics. These dynamics can be visualized as flow fields and attractors, enabling direct tests of attractor-based theories of neural computation. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.
BibTeX
@inproceedings{
kim2025flowfield,
title={Flow-field inference from neural data using deep recurrent networks},
author={Timothy Doyeon Kim and Thomas Zhihao Luo and Tankut Can and Kamesh Krishnamurthy and Jonathan W. Pillow and Carlos D Brody},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=ZLC4B9oQWX}
}