Scalable spike sorting across thousands of neurons by modeling neural dynamics with NeuroSort

Abstract

Neural circuit research requires resolving the functional coding mechanisms at the single-neuron scale. However, neural signals captured from extracellular recordings are inherently complex, shaped both by electrode properties and the spatially distributed neural population activity across recording sites. These factors introduce strong signal mixing and waveform variation, making it diffcult for existing spike sorting methods to reliably isolate individual neurons from large-scale recodings. To address this, we develop NeuroSort, which fuses spatiotemporal information through a contrastive learning strategy to adaptively model neural dynamics. NeuroSort is a GPU-enabled, highly efficient spike sorting framework, enabling the isolating of up to thousands of neurons. We validated NeuroSort on in vivo extracellular recordings, including Utah array data from rhesus macaques performing motor tasks and Neuropixels 1.0 probe data from a rat during rest. Moreover, in a recording collected by the 1,024-channel Neuroscroll probe, NeuroSort sorted the largest number of well-isolated neurons from different brain areas while achieving a 2-8 times speedup over other methods. Taken together, NeuroSort generalizes across recording conditions, providing an interpretable and scalable paradigm for whole-brain dynamic analysis.