Flow-field inference from neural data using deep recurrent networks
Kim, T. D.; Luo, T. Z.; Can, T.; Krishnamurthy, K.; Pillow, J. W.; Brody, C. D.
Show abstract
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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Scalable gaussian process inference of neural responses to natural images 97%
- Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior 96%
- Predicting the unseen: a diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.