Paper Info
Title
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Abstract
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time. We show how this approach naturally handles missing data and multiple partial recordings, and can identify dynamics and predict correlations even in the presence of severe subsampling and small overlap between recordings. We demonstrate the effectiveness of the approach both on simulated data and a whole-brain larval zebrafish imaging dataset.
Year
Venue
Field
2017
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
Population,Data mining,Stochastic gradient descent,Dimensionality reduction,Computer science,Dynamical systems theory,Artificial intelligence,Missing data,Estimation theory,Machine learning,Scalability
DocType
Volume
ISSN
Conference
30
1049-5258
Citations 
PageRank 
References 
2
0.40
9
Authors
3
Name
Order
Citations
PageRank
Nonnenmacher Marcel191.19
Srinivas C. Turaga212723.75
Jakob H Macke315814.15