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Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping.
Williams, Alex H; Poole, Ben; Maheswaranathan, Niru; Dhawale, Ashesh K; Fisher, Tucker; Wilson, Christopher D; Brann, David H; Trautmann, Eric M; Ryu, Stephen; Shusterman, Roman; Rinberg, Dmitry; Ölveczky, Bence P; Shenoy, Krishna V; Ganguli, Surya.
Afiliação
  • Williams AH; Neuroscience Program, Stanford University, Stanford, CA 94305, USA. Electronic address: ahwillia@stanford.edu.
  • Poole B; Google Brain, Google Inc., Mountain View, CA 94043, USA.
  • Maheswaranathan N; Google Brain, Google Inc., Mountain View, CA 94043, USA.
  • Dhawale AK; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
  • Fisher T; Neuroscience Program, Stanford University, Stanford, CA 94305, USA.
  • Wilson CD; Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA.
  • Brann DH; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
  • Trautmann EM; Neuroscience Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
  • Ryu S; Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA.
  • Shusterman R; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
  • Rinberg D; 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.
  • Ölveczky BP; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
  • Shenoy KV; 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 Stanfo
  • Ganguli S; 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 Stanf
Neuron ; 105(2): 246-259.e8, 2020 01 22.
Article em En | MEDLINE | ID: mdl-31786013
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Potenciais de Ação / Reconhecimento Automatizado de Padrão / Neurônios Limite: Animals Idioma: En Revista: Neuron Ano de publicação: 2020 Tipo de documento: Article
Texto completo
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PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Potenciais de Ação / Reconhecimento Automatizado de Padrão / Neurônios Limite: Animals Idioma: En Revista: Neuron Ano de publicação: 2020 Tipo de documento: Article