Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

eLife

Chandramouli Chandrasekaran¹  Hymavathy Balasubramanian²  Alexandra Tsolias³  Maria Medalla³  Krishna V Shenoy⁴  Eric Kenji Lee⁵  Stephanie Udochukwu Anakwe⁶ 

[1] Department of Bioengineering, Stanford University, Stanford, United States;Bernstein Center for Computational Neuroscience, Bernstein Center for Computational Neuroscience, Berlin, Germany;Department of Anatomy and Neurobiology, Boston University, Boston, United States;Department of Electrical Engineering, Stanford University, Stanford, United States;Psychological and Brain Sciences, Boston University, Boston, United States;Undergraduate Program in Neuroscience, Boston University, Boston, United States;

关键词: nonlinear dimensionality reduction;    waveforms;    cell types;    circuits;    layers;   

DOI  :  10.7554/eLife.67490

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown