RT Journal Article SR Electronic T1 Flow-field inference from neural data using deep recurrent networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.11.14.567136 DO 10.1101/2023.11.14.567136 A1 Kim, Timothy Doyeon A1 Luo, Thomas Zhihao A1 Can, Tankut A1 Krishnamurthy, Kamesh A1 Pillow, Jonathan W. A1 Brody, Carlos D. YR 2023 UL http://biorxiv.org/content/early/2023/11/16/2023.11.14.567136.abstract AB Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of 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 that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity. Using population spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR outperforms existing methods in capturing the heterogeneous responses of individual neurons. We further show that FINDR can discover interpretable low-dimensional dynamics when it is trained to disentangle task-relevant and irrelevant components of the neural population activity. Importantly, the low-dimensional nature of the learned dynamics allows for explicit visualization of flow fields and attractor structures. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.Competing Interest StatementThe authors have declared no competing interest.