RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies
With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until n...
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| Published in | PloS one Vol. 19; no. 12; p. e0312438 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
05.12.2024
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0312438 |
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| Abstract | With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort’s true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort’s performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort’s functionality on different types of recording hardware and electrode configurations. |
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| AbstractList | With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort’s true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort’s performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort’s functionality on different types of recording hardware and electrode configurations. With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort's true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort's performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort's functionality on different types of recording hardware and electrode configurations.With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort's true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort's performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort's functionality on different types of recording hardware and electrode configurations. |
| Author | Bartram, Julian Petzold, Linda R. Cheng, Zhuowei Lim, Max Robbins, Ash Tovar, Kenneth R. Hansma, Paul K. Haussler, David Parks, David F. Kosik, Kenneth S. van der Molen, Tjitse Hierlemann, Andreas |
| AuthorAffiliation | 5 Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America 3 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland 7 Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California, United States of America 8 UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, California, United States of America 1 Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, California, United States of America 4 Department of Computer Science, University of California Santa Barbara, Santa Barbara, California, United States of America 6 Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America 9 Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America University of British Columbia, CANADA 2 Depar |
| AuthorAffiliation_xml | – name: 8 UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, California, United States of America – name: 9 Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America – name: 2 Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, California, United States of America – name: 6 Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America – name: University of British Columbia, CANADA – name: 3 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland – name: 4 Department of Computer Science, University of California Santa Barbara, Santa Barbara, California, United States of America – name: 1 Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, California, United States of America – name: 7 Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California, United States of America – name: 5 Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America |
| Author_xml | – sequence: 1 givenname: Tjitse orcidid: 0000-0001-6147-542X surname: van der Molen fullname: van der Molen, Tjitse – sequence: 2 givenname: Max orcidid: 0009-0003-1403-7875 surname: Lim fullname: Lim, Max – sequence: 3 givenname: Julian surname: Bartram fullname: Bartram, Julian – sequence: 4 givenname: Zhuowei surname: Cheng fullname: Cheng, Zhuowei – sequence: 5 givenname: Ash surname: Robbins fullname: Robbins, Ash – sequence: 6 givenname: David F. orcidid: 0000-0002-4635-8206 surname: Parks fullname: Parks, David F. – sequence: 7 givenname: Linda R. surname: Petzold fullname: Petzold, Linda R. – sequence: 8 givenname: Andreas surname: Hierlemann fullname: Hierlemann, Andreas – sequence: 9 givenname: David surname: Haussler fullname: Haussler, David – sequence: 10 givenname: Paul K. surname: Hansma fullname: Hansma, Paul K. – sequence: 11 givenname: Kenneth R. surname: Tovar fullname: Tovar, Kenneth R. – sequence: 12 givenname: Kenneth S. orcidid: 0000-0003-3224-5179 surname: Kosik fullname: Kosik, Kenneth S. |
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| Copyright | Copyright: © 2024 van der Molen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2024 van der Molen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2024 van der Molen et al 2024 van der Molen et al 2024 van der Molen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Action potential Action Potentials - physiology Algorithms Animals Artificial neural networks Axons Biology and Life Sciences Closed loops Configuration management Delay time Electrodes Experiments Firing pattern Neural networks Neural Networks, Computer Neurons Neurons - physiology Physical Sciences Propagation Real time Recording Research and Analysis Methods Sorting algorithms Waveforms |
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| Title | RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies |
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