Extracellular Spike Waveforms: Morphology, Biophysics, and Classification Strategies

Abstract

Extracellular spike waveforms provide critical insights into neuronal activity, morphology, and function. Their shape can reveal cell-type identity, excitatory versus inhibitory function, and afferent projections from distal regions. The development of dense, high-channel-count probes now permits recordings from thousands of sites simultaneously, revealing a wider diversity of waveform types than previously appreciated. These advances provide an unprecedented opportunity to link waveform shape to the underlying biophysical processes of neurons and their spatial arrangement relative to the recording electrode. This review examines and catalogs the diversity of extracellular waveforms (including negative, triphasic, and positive spike waveforms), focusing on their biophysical origins and roles in neural compartments. We also discuss classification strategies, ranging from traditional feature-based approaches that use specific waveform features (such as spike duration and peak-to-trough ratios) to emerging machine learning and multimodal methods that integrate waveform shape with firing dynamics and anatomical localization. These new approaches reveal novel neuronal populations but also highlight a pressing need for standardized classification frameworks to ensure reproducibility and facilitate cross-study comparisons. Finally, we review how experimental factors such as filtering, sampling biases, and spike-sorting algorithms shape the observed diversity of extracellular waveforms. By consolidating recent progress in both experimental and computational approaches, this review provides a comprehensive resource for interpreting extracellular recordings. A deeper understanding of waveform diversity will advance basic neuroscience and accelerate applications in brain–machine interfaces, diagnostics, and neural prosthetics.