@article {70251,
	title = {SCREWx: A Screwless, Chronic, Recoverable, and Lightweight Neuropixels fixture for freely-moving rodents},
	journal = {bioRxiv},
	year = {2026},
	month = {2026 Jan 06},
	abstract = {<p>High-density Neuropixels probes enable the study of large neural populations with single-cell and sub-millisecond resolution. While single-probe and acute head-fixed experiments have yielded critical scientific insights, understanding the neural mechanisms underlying many complex behaviors requires simultaneous multi-region recordings in freely moving, chronically implanted animals. Various probe fixtures have been developed to enable high-density recording, but existing designs impose critical limitations: their substantial weight restricts the maximum probe count that smaller animals can support, their bulky dimensions constrain the proximity of targeted brain regions, and their complex assembly risks damaging the probe during insertion and recovery. In this paper, we present a lightweight, fully 3D-printable, compact, and screwless fixture for chronic Neuropixels implants in freely moving rodents that features simple mechanisms for stable implantation and safe extraction. Our fixture design enables stable, high-yield single-unit recordings for months-long experiments, along with an 83\% successful probe extraction rate. This fixture design provides a robust and accessible solution for long-term, multi-probe chronic Neuropixels recordings, increasing experimental throughput and enabling more complex experimental designs to investigate brain-wide neural dynamics.</p>
},
	doi = {10.64898/2026.01.06.697790},
	url = {https://www.biorxiv.org/content/early/2026/01/06/2026.01.06.697790},
	author = {Cheng, Alexandra and DeWeese, Tate and Zhou, Yueqing and Sueoka, Yotaro and Koukuntla, Sai and Green, Merrill and Cullen, Kathleen and Knierim, James and Graves, Austin and Harris, Timothy}
}
@article {70004,
	title = {SLAy-ing oversplitting errors in high-density electrophysiology spike sorting},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Jun 25},
	abstract = {<p>The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods are subject to assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic errors. Consequently, manual curation of these errors, which is both time-consuming and lacks reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce here the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike clusters. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, time-shift invariant low-dimensional waveform representations, and (2) a cross-correlogram significance metric based on the earth-movers distance between the observed and null cross-correlograms. We demonstrate that SLAy achieves 85\% agreement with human curators across a diverse set of animal models, brain regions, and probe geometries. To illustrate the impact of spike sorting errors on downstream analyses, we develop a new burst-detection algorithm and show that SLAy fixes spike sorting errors that preclude the accurate detection of bursts in neural data. SLAy leverages GPU parallelization and multithreading for computational efficiency, and is compatible with Phy and NeuroData Without Borders, making it a practical and flexible solution for large-scale ephys data analysis.</p>
},
	doi = {10.1101/2025.06.20.660590},
	url = {https://www.biorxiv.org/content/early/2025/06/25/2025.06.20.660590},
	author = {Koukuntla, Sai and DeWeese, Tate and Cheng, Alexandra and Mildren, Robyn and Lawrence, Aamna and Graves, Austin R and Colonell, Jennifer and Harris, Timothy D and Charles, Adam S}
}
