Journal Article

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

Lee, Eric Kenji, Shenoy, Krishna V, Anakwe, Stephanie Udochukwu, Chandrasekaran, Chandramouli, Tsolias, Alexandra, Balasubramanian, Hymavathy, Medalla, Maria

2021

Overview

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.

• Publisher: eLife Sciences Publications Ltd,eLife Sciences Publications, Ltd
• Subject: Animals / Behavior / cell types / circuits / Cortex (premotor) / Decision making / Decision Making - physiology / Firing rate / layers / Macaca mulatta / Machine Learning / Male / Mental task performance / Motor Cortex - cytology / Motor Cortex - physiology / Neural Pathways - physiology / Neurons / Neurons - physiology / Neuroscience / nonlinear dimensionality reduction / Nonlinear Dynamics / Physiology / Software / Task Performance and Analysis / waveforms