Publication:

Multi-scale neural decoding and analysis

Date

Date

Date
2021
Journal Article
Published version
cris.lastimport.scopus2025-06-10T03:38:05Z
cris.lastimport.wos2025-07-24T01:34:22Z
cris.virtual.orcidhttps://orcid.org/0000-0002-4915-9448
cris.virtualsource.orcid8fcd2840-73f2-4603-90e1-9826c7f806fc
dc.contributor.institutionUniversity of Zurich
dc.date.accessioned2021-08-30T15:27:54Z
dc.date.available2021-08-30T15:27:54Z
dc.date.issued2021-08-01
dc.description.abstract

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.

dc.identifier.doi10.1088/1741-2552/ac160f
dc.identifier.issn1741-2552
dc.identifier.othermerlin-id:21468
dc.identifier.scopus2-s2.0-85114054779
dc.identifier.urihttps://www.zora.uzh.ch/handle/20.500.14742/185033
dc.identifier.wos000685175800001
dc.language.isoeng
dc.subjectmulti-scale analyses
dc.subjectneural decoding
dc.subjectelectrophysiology
dc.subjectfunctional imaging
dc.subject.ddc330 Economics
dc.title

Multi-scale neural decoding and analysis

dc.typearticle
dcterms.accessRightsinfo:eu-repo/semantics/openAccess
dcterms.bibliographicCitation.journaltitleJournal of Neural Engineering
dcterms.bibliographicCitation.number045013
dcterms.bibliographicCitation.originalpublishernameIOP Publishing
dcterms.bibliographicCitation.pagestartonline
dcterms.bibliographicCitation.volume18
dspace.entity.typePublicationen
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationRice University
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationRice University
uzh.contributor.affiliationUniversity of Zurich
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationUniversity of Washington, Seattle, Washington National Primate Research Center
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.affiliationThe University of Texas at Austin
uzh.contributor.authorLu, Hung-Yun
uzh.contributor.authorLorenc, Elizabeth S
uzh.contributor.authorZhu, Hanlin
uzh.contributor.authorKilmarx, Justin
uzh.contributor.authorSulzer, James
uzh.contributor.authorXie, Chong
uzh.contributor.authorTobler, Philippe N
uzh.contributor.authorWatrous, Andrew J
uzh.contributor.authorOrsborn, Amy L
uzh.contributor.authorLewis-Peacock, Jarrod
uzh.contributor.authorSantacruz, Samantha R
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceNo
uzh.contributor.correspondenceYes
uzh.document.availabilitypublished_version
uzh.eprint.datestamp2021-08-30 15:27:54
uzh.eprint.lastmod2025-07-24 01:40:41
uzh.eprint.statusChange2021-08-30 15:27:54
uzh.harvester.ethYes
uzh.harvester.nbNo
uzh.identifier.doi10.5167/uzh-205770
uzh.jdb.eprintsId10568
uzh.oastatus.unpaywallgreen
uzh.oastatus.zoraGreen
uzh.publication.citationLu, Hung-Yun; Lorenc, Elizabeth S; Zhu, Hanlin; Kilmarx, Justin; Sulzer, James; Xie, Chong; Tobler, Philippe N; Watrous, Andrew J; Orsborn, Amy L; Lewis-Peacock, Jarrod; Santacruz, Samantha R (2021). Multi-scale neural decoding and analysis. Journal of Neural Engineering, 18(045013):online.
uzh.publication.originalworkoriginal
uzh.publication.publishedStatusfinal
uzh.publication.scopedisciplinebased
uzh.scopus.impact25
uzh.scopus.subjectsBiomedical Engineering
uzh.scopus.subjectsCellular and Molecular Neuroscience
uzh.workflow.chairSubjectoecECON1
uzh.workflow.doajuzh.workflow.doaj.false
uzh.workflow.eprintid205770
uzh.workflow.fulltextStatuspublic
uzh.workflow.revisions43
uzh.workflow.rightsCheckkeininfo
uzh.workflow.sourceCrossRef:10.1088/1741-2552/ac160f
uzh.workflow.statusarchive
uzh.wos.impact27
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