Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex

Cortical circuits involved in decision-making are thought to contain a large number of cell types--each with different physiological, functional, and laminar distribution properties--that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features, such as trough to peak duration of extracellular spikes, to identify putative cell types these but can only capture 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 also revealing undocumented diversity. These clusters exhibit distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. By revealing additional cell type diversity, WaveMAP facilitates a more nuanced understanding of how the dynamics of cell types unfolds across cortical layers during decision-making.