Density-based longitudinal neuron tracking in high-density electrophysiological recordings
Jun. 17, 2026
Dr. Jianing Yu published a review in Patterns.
Tracking single neurons across days in high-density extracellular recordings is essential for establishing neural mechanisms of learning, memory, and post-injury recovery. However, in weeks-long recordings, identifying cross-day matches among thousands of units is confounded by changes in spike waveforms, unit turnover, and representational drift in neural responses. We introduce DANT (Density-based Across-day Neuron Tracking), an unsupervised framework that jointly estimates probe motion and neuron identity by alternating between density-based clustering in feature space and probe-motion correction inferred from provisional matches. Estimated drift is used to re-register spike waveforms across sessions, after which clustering is recomputed; this iterative loop continues until the set of matches stabilizes. In parallel, DANT learns a decision boundary from match and non-match assignments derived from the clustering results, enabling it to reject low-similarity candidate pairs. Applied to weeks-long Neuropixels recordings from the cortex and striatum in freely moving rats during stable behavior and task switching, DANT substantially increases match yield and reduces false negatives while maintaining a low false-positive rate relative to existing approaches. Together, these results indicate that DANT provides a general, unsupervised solution for longitudinal tracking in chronic, high-density recordings.
Original link: https://doi.org/10.1016/j.patter.2026.101590