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Multi-scale neural decoding and analysis
Author(s) -
Hung-Yun Lu,
Elizabeth S. Lorenc,
Hanlin Zhu,
Justin Kilmarx,
James Sulzer,
Chong Xie,
Philippe N. Tobler,
Andrew J. Watrous,
Amy L. Orsborn,
Jarrod A. Lewis-Peacock,
Samantha R. Santacruz
Publication year - 2021
Publication title -
journal of neural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.594
H-Index - 111
eISSN - 1741-2560
pISSN - 1741-2552
DOI - 10.1088/1741-2552/ac160f
Subject(s) - computer science , modalities , modality (human–computer interaction) , neural decoding , scale (ratio) , artificial intelligence , temporal scales , decoding methods , data science , cognitive science , machine learning , psychology , telecommunications , social science , ecology , physics , quantum mechanics , sociology , biology
Objective . Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach . We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results . We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance . This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

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