@article {69350,
	title = {Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Apr 10},
	abstract = {<p>To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ( 10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron{\textquoteright}s electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to 3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75\% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.</p>
},
	doi = {10.1101/2023.08.23.554527},
	url = {https://www.biorxiv.org/content/early/2024/04/10/2023.08.23.554527},
	author = {Zhiwen Ye and Andrew M Shelton and Jordan R Shaker and Julien M Boussard and Jennifer Colonell and Daniel Birman and Sahar Manavi and Susu Chen and Charlie Windolf and Cole Hurwitz and Tomoyuki Namima and Frederico Pedraja and Shahaf Weiss and Bogdan Raducanu and Torbj{\o}rn Ness and Xiaoxuan Jia and Giulia Mastroberardino and L. Federico Rossi and Matteo Carandini and Michael Hausser and Gaute T Einevoll and Gilles Laurent and Nathaniel B Sawtell and Wyeth Bair and Anitha Pasupathy and Carolina Mora-Lopez and Barun Dutta and Liam Paninski and Joshua H Siegle and Christof Koch and Shawn R Olsen and Timothy D Harris and Nicholas A Steinmetz}
}
@article {68917,
	title = {Pinpoint: trajectory planning for multi-probe electrophysiology and injections in an interactive web-based 3D environment},
	journal = {bioRxiv},
	year = {2023},
	month = {2023 Jul 15},
	abstract = {<p>Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often can{\textquoteright}t be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoint{\textquoteright}s easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.</p>
},
	doi = {10.1101/2023.07.14.548952},
	url = {https://doi.org/10.1101/2023.07.14.548952},
	author = {Daniel Birman and Kenneth J. Yang and Steven J. West and Bill Karsh and Yoni Browning and the International Brain Laboratory and Joshua H. Siegle and Nicholas A. Steinmetz}
}
