WaveMAP for identifying putative cell types from in vivo electrophysiology

Kenji Lee; Nicole Carr; Alec Perliss; Chandramouli Chandrasekaran

doi:10.1016/j.xpro.2023.102320

WaveMAP for identifying putative cell types from in vivo electrophysiology

STAR Protoc. 2023 May 22;4(2):102320. doi: 10.1016/j.xpro.2023.102320. Online ahead of print.

Authors

Kenji Lee¹, Nicole Carr², Alec Perliss², Chandramouli Chandrasekaran³

Affiliations

¹ Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA. Electronic address: [email protected].
² Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
³ Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian, School of Medicine, Boston, MA 02118, USA; Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA. Electronic address: [email protected].

Abstract

Action potential spike widths are used to classify cell types as either excitatory or inhibitory; however, this approach obscures other differences in waveform shape useful for identifying more fine-grained cell types. Here, we present a protocol for using WaveMAP to generate nuanced average waveform clusters more closely linked to underlying cell types. We describe steps for installing WaveMAP, preprocessing data, and clustering waveform into putative cell types. We also detail cluster evaluation for functional differences and interpretation of WaveMAP output. For complete details on the use and execution of this protocol, please refer to Lee et al. (2021).¹.

Keywords: Behavior; Cognitive Neuroscience.

Abstract

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