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Neuron. 2019 Nov 15. pii: S0896-6273(19)30894-3. doi: 10.1016/j.neuron.2019.10.020. [Epub ahead of print]

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping.

Author information

1
Neuroscience Program, Stanford University, Stanford, CA 94305, USA. Electronic address: ahwillia@stanford.edu.
2
Google Brain, Google Inc., Mountain View, CA 94043, USA.
3
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
4
Neuroscience Program, Stanford University, Stanford, CA 94305, USA.
5
Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA.
6
Neuroscience Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
7
Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA.
8
Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
9
Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10016, USA.
10
Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
11
Applied Physics Department, Stanford University, Stanford, CA 94305, USA; Neurobiology Department, Stanford University, Stanford, CA 94305, USA; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Google Brain, Google Inc., Mountain View, CA 94043, USA. Electronic address: sganguli@stanford.edu.

Abstract

Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.

KEYWORDS:

Exploratory Data Analysis; Neural Oscillations; Population Dynamics; Spike Train Analysis; Statistics; Time Warping; Unsupervised Learning

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