Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.
@article{WiKiWaVy18,
author = {Williams, Alex H. and Kim, Tony Hyun and Wang, Forea and Vyas, Saurabh and Ryu, Stephen I. and Shenoy, Krishna V. and Schnitzer, Mark and Kolda, Tamara G. and Ganguli, Surya},
title = {Unsupervised Discovery of Demixed, Low-dimensional Neural Dynamics across Multiple Timescales through Tensor Components Analysis},
journal = {Neuron},
volume = {98},
number = {6},
pages = {1099-1115},
year = {2018},
doi = {10.1016/j.neuron.2018.05.015},
}