Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex
Abstract
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 06, 2021
- Source ID
- 10.7554/elife.67490
Entities
People
- Alexandra Tsolias
- Chandramouli Chandrasekaran
- Eric Lee
- Hymavathy Balasubramanian
- Krishna Shenoy
- Maria Medalla
- Stephanie Anakwe
Organizations
- Bernstein Network
- Boston University
- Brain & Behavior Research Foundation
- Defense Advanced Research Projects Agency
- Howard Hughes Medical Institute
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- National Institute on Deafness and Other Communication Disorders
- Office of Naval Research
- Office of the Director
- Simons Foundation
- Stanford University
- Stanford University School of Engineering
- Whitehall Foundation