Relevant bibliographies by topics / Neuropixels

Academic literature on the topic 'Neuropixels'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Neuropixels"

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Steinmetz, Nicholas A., Christof Koch, Kenneth D. Harris, and Matteo Carandini. "Challenges and opportunities for large-scale electrophysiology with Neuropixels probes." Current Opinion in Neurobiology 50 (June 2018): 92–100. http://dx.doi.org/10.1016/j.conb.2018.01.009.

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Steinmetz, Nicholas. "Large-scale electrophysiology with Neuropixels: Scientific advances and future directions." IBRO Reports 6 (September 2019): S43. http://dx.doi.org/10.1016/j.ibror.2019.07.131.

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Steinmetz, Nicholas A., Cagatay Aydin, Anna Lebedeva, Michael Okun, Marius Pachitariu, Marius Bauza, Maxime Beau, et al. "Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings." Science 372, no. 6539 (April 15, 2021): eabf4588. http://dx.doi.org/10.1126/science.abf4588.

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Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.
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Paulk, Angelique C., Yoav Kfir, Arjun R. Khanna, Martina L. Mustroph, Eric M. Trautmann, Dan J. Soper, Sergey D. Stavisky, et al. "Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex." Nature Neuroscience 25, no. 2 (January 31, 2022): 252–63. http://dx.doi.org/10.1038/s41593-021-00997-0.

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Putzeys, Jan, Silke Musa, Carolina Mora Lopez, Bogdan C. Raducanu, Alain Carton, Jef De Ceulaer, Bill Karsh, et al. "Neuropixels Data-Acquisition System: A Scalable Platform for Parallel Recording of 10 000+ Electrophysiological Signals." IEEE Transactions on Biomedical Circuits and Systems 13, no. 6 (December 2019): 1635–44. http://dx.doi.org/10.1109/tbcas.2019.2943077.

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van Daal, Rik J. J., Çağatay Aydin, Frédéric Michon, Arno A. A. Aarts, Michael Kraft, Fabian Kloosterman, and Sebastian Haesler. "Implantation of Neuropixels probes for chronic recording of neuronal activity in freely behaving mice and rats." Nature Protocols 16, no. 7 (June 9, 2021): 3322–47. http://dx.doi.org/10.1038/s41596-021-00539-9.

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Chintaluri, Chaitanya, Marta Bejtka, Władysław Średniawa, Michał Czerwiński, Jakub M. Dzik, Joanna Jędrzejewska-Szmek, Kacper Kondrakiewicz, Ewa Kublik, and Daniel K. Wójcik. "What we can and what we cannot see with extracellular multielectrodes." PLOS Computational Biology 17, no. 5 (May 14, 2021): e1008615. http://dx.doi.org/10.1371/journal.pcbi.1008615.

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Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. We show that with conceptual separation of the estimation space from experimental setup one can recover sources not accessible to standard methods.
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Klein, Natalie, Joshua H. Siegle, Tobias Teichert, and Robert E. Kass. "Cross-population coupling of neural activity based on Gaussian process current source densities." PLOS Computational Biology 17, no. 11 (November 17, 2021): e1009601. http://dx.doi.org/10.1371/journal.pcbi.1009601.

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Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs.
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Armstrong, Lawrence E., and Stavros A. Kavouras. "Thirst and Drinking Paradigms: Evolution from Single Factor Effects to Brainwide Dynamic Networks." Nutrients 11, no. 12 (November 22, 2019): 2864. http://dx.doi.org/10.3390/nu11122864.

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The motivation to seek and consume water is an essential component of human fluid–electrolyte homeostasis, optimal function, and health. This review describes the evolution of concepts regarding thirst and drinking behavior, made possible by magnetic resonance imaging, animal models, and novel laboratory techniques. The earliest thirst paradigms focused on single factors such as dry mouth and loss of water from tissues. By the end of the 19th century, physiologists proposed a thirst center in the brain that was verified in animals 60 years later. During the early- and mid-1900s, the influences of gastric distention, neuroendocrine responses, circulatory properties (i.e., blood pressure, volume, concentration), and the distinct effects of intracellular dehydration and extracellular hypovolemia were recognized. The majority of these studies relied on animal models and laboratory methods such as microinjection or lesioning/oblation of specific brain loci. Following a quarter century (1994–2019) of human brain imaging, current research focuses on networks of networks, with thirst and satiety conceived as hemispheric waves of neuronal activations that traverse the brain in milliseconds. Novel technologies such as chemogenetics, optogenetics, and neuropixel microelectrode arrays reveal the dynamic complexity of human thirst, as well as the roles of motivation and learning in drinking behavior.
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Luo, Thomas Zhihao, Adrian Gopnik Bondy, Diksha Gupta, Verity Alexander Elliott, Charles D. Kopec, and Carlos D. Brody. "An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats." eLife 9 (October 22, 2020). http://dx.doi.org/10.7554/elife.59716.

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The use of Neuropixels probes for chronic neural recordings is in its infancy and initial studies leave questions about long-term stability and probe reusability unaddressed. Here, we demonstrate a new approach for chronic Neuropixels recordings over a period of months in freely moving rats. Our approach allows multiple probes per rat and multiple cycles of probe reuse. We found that hundreds of units could be recorded for multiple months, but that yields depended systematically on anatomical position. Explanted probes displayed a small increase in noise compared to unimplanted probes, but this was insufficient to impair future single-unit recordings. We conclude that cost-effective, multi-region, and multi-probe Neuropixels recordings can be carried out with high yields over multiple months in rats or other similarly sized animals. Our methods and observations may facilitate the standardization of chronic recording from Neuropixels probes in freely moving animals.
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Conference papers on the topic "Neuropixels"

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Jia, Xiaoxuan, Joshua Siegle, Yazan Billeh, Séverine Durand, Greggory Heller, Tamina Ramirez, Anton Arkhipov, and Shawn Olsen. "Subnetworks mediating feedforward and feedback processes revealed by multi-area Neuropixels recordings." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1281-0.

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Dutta, B., A. Andrei, T. D. Harris, C. M. Lopez, J. O'Callahan, J. Putzeys, B. C. Raducanu, et al. "The Neuropixels probe: A CMOS based integrated microsystems platform for neuroscience and brain-computer interfaces." In 2019 IEEE International Electron Devices Meeting (IEDM). IEEE, 2019. http://dx.doi.org/10.1109/iedm19573.2019.8993611.

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Zhang, Zheng, and Timothy G. Constandinou. "A robust and automated algorithm that uses single-channel spike sorting to label multi-channel Neuropixels data." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441234.

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Varol, Erdem, Julien Boussard, Nishchal Dethe, Olivier Winter, Anne Urai, The International Brain Laboratory, Anne Churchland, Nick Steinmetz, and Liam Paninski. "Decentralized Motion Inference and Registration of Neuropixel Data." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414145.

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Wang, Qifan, Jiapeng Yin, and He Cui. "Reinforcement of Neuropixels probes for high-density neural recording in non-human primates**This work is supported by National Key RD Program of China (Grant No. 2017YFA0701102), Shanghai Science and Technology Committee, China (Grant No. 18JC1415102), Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX05), National Natural Science Foundation of China (Grant No. 31800869), and Strategic Priority Research Program of Chinese Academy of Science (Grant No. XDB32040100)." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441229.

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Reports on the topic "Neuropixels"

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Juavinett, Ashley, George Bekheet, and Anne Churchland. Chronically-implanted Neuropixels probes enable high yield recordings in freely moving mice: dataset. Cold Spring Harbor Laboratory, August 2019. http://dx.doi.org/10.14224/1.38304.

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Journal articles
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