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Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.
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Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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Buccino AP, Hurwitz CL, Garcia S, Magland J, Siegle JH, Hurwitz R, Hennig MH. SpikeInterface, a unified framework for spike sorting. eLife 2020; 9:e61834. [PMID: 33170122 PMCID: PMC7704107 DOI: 10.7554/elife.61834] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/09/2020] [Indexed: 12/21/2022] Open
Abstract
Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.
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Affiliation(s)
- Alessio P Buccino
- Department of Biosystems Science and Engineering, ETH ZurichZürichSwitzerland
- Centre for Integrative Neuroplasticity (CINPLA), University of OsloOsloNorway
| | - Cole L Hurwitz
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRSLyonFrance
| | | | | | | | - Matthias H Hennig
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
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ABE-VIEW: Android Interface for Wireless Data Acquisition and Control. SENSORS 2018; 18:s18082647. [PMID: 30104474 PMCID: PMC6111993 DOI: 10.3390/s18082647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 08/04/2018] [Accepted: 08/09/2018] [Indexed: 01/19/2023]
Abstract
Advances in scientific knowledge are increasingly supported by a growing community of developers freely sharing new hardware and software tools. In this spirit we have developed a free Android app, ABE-VIEW, that provides a flexible graphical user interface (GUI) populated entirely from a remote instrument by ascii-coded instructions communicated wirelessly over Bluetooth. Options include an interactive chart for plotting data in real time, up to 16 data fields, and virtual controls including buttons, numerical controls with user-defined range and resolution, and radio buttons which the user can use to send coded instructions back to the instrument. Data can be recorded into comma delimited files interactively at the user’s discretion. Our original objective of the project was to make data acquisition and control for undergraduate engineering labs more modular and affordable, but we have also found that the tool is highly useful for rapidly testing novel sensor systems for iterative improvement. Here we document the operation of the app and syntax for communicating with it. We also illustrate its application in undergraduate engineering labs on dynamic systems modeling, as well as for identifying the source of harmonic distortion affecting electrochemical impedance measurements at certain frequencies in a novel wireless potentiostat.
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