Department of Electrical Engineering, Stanford University, Stanford, United States; Department of Bioengineering, Stanford University, Stanford, United States; Department of Neurobiology, Stanford University, Stanford, United States; Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States; Bio-X Institute, Stanford University, Stanford, United States; Howard Hughes Medical Institute, Stanford University, Stanford, United States
Psychological and Brain Sciences, Boston University, Boston, United States; Department of Anatomy and Neurobiology, Boston University, Boston, United States; Center for Systems Neuroscience, Boston University, Boston, United States; Department of Biomedical Engineering, Boston University, Boston, United States
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.
