Low-latency single channel real-time neural spike sorting system based on template matching

Autoři: Pan Ke Wang aff001;  Sio Hang Pun aff001;  Chang Hao Chen aff001;  Elizabeth A. McCullagh aff003;  Achim Klug aff003;  Anan Li aff004;  Mang I. Vai aff001;  Peng Un Mak aff002;  Tim C. Lei aff001
Působiště autorů: State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, China aff001;  Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China aff002;  Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America aff003;  Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China aff004;  Department of Electrical Engineering, University of Colorado, Denver, CO, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225138


Recent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.

Klíčová slova:

Action potentials – Algorithms – Computer hardware – Electrode recording – Electrodes – Gaussian noise – Mice – Neurons


1. Humphrey DR, Schmidt EM. Extracellular single-unit recording methods. In: Boulton AA, Baker GB, Vanderwolf CH, editors. Neurophysiological Techniques: Applications to Neural Systems. Totowa, NJ: Humana Press; 1990. pp. 1–64. doi: 10.1385/0-89603-185-3:1

2. Williams M. Electrophysiological techniques. Curr Protoc Pharmacol. 2007; 10–12. doi: 10.1002/0471141755.ph1100s39

3. Cuevas J. Electrophysiological recording techniques. Reference Module in Biomedical Research. 2014. doi: 10.1016/B978-0-12-801238-3.04997–7

4. Wickenden AD. Overview of Electrophysiological Techniques. Curr Protoc Pharmacol. 2014;64: 11.1.1–17. doi: 10.1002/0471141755.ph1101s64 26344208

5. Bretschneider F, Jan R. de Weille. Introduction to electrophysiological methods and instrumentation. Book. 2006. doi: 10.1201/9781439823798

6. Brette R, Destexhe A. Handbook of Neural Activity Measurement. Cambridge University Press; 2012. Available: https://books.google.com/books?id=YLyGmfVuBsIC&pgis=1

7. Chen CH, Pun SH, Mak PU, Vai MI, Klug A, Lei TC. Circuit models and experimental noise measurements of micropipette amplifiers for extracellular neural recordings from live animals. Biomed Res Int. 2014;2014. doi: 10.1155/2014/135026 25133158

8. Chen CH, McCullagh EA, Pun SH, Mak PU, Vai MI, Mak PI, et al. An integrated circuit for simultaneous extracellular electrophysiology recording and optogenetic neural manipulation. IEEE Trans Biomed Eng. 2017;64: 557–568. doi: 10.1109/TBME.2016.2609412 28221990

9. Rey HG, Pedreira C, Quian Quiroga R. Past, present and future of spike sorting techniques. Brain Res Bull. 2015;119: 106–117. doi: 10.1016/j.brainresbull.2015.04.007 25931392

10. Gibson S, Judy JW, Markovic D. Comparison of spike-sorting algorithms for future hardware implementation. Conf Proc IEEE Eng Med Biol Soc. 2008;2008: 5015–5020. doi: 10.1109/IEMBS.2008.4650340 19163843

11. Lewicki MS. A review of methods for spike sorting: the detection and classification of neural action potentials. Network. 1998;9: R53–78. doi: 10.1088/0954-898X/9/4/001 10221571

12. Kadir SN, Goodman DFM, Harris KD. High-dimensional cluster analysis with the masked EM algorithm. Neural Comput. 2014/08/22. 2014;26: 2379–2394. doi: 10.1162/NECO_a_00661 25149694

13. Aksenova TI, Chibirova OK, Dryga OA, Tetko I V, Benabid A-L, Villa AEP. An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals. Methods. 2003;30: 178–187. doi: 10.1016/s1046-2023(03)00079-3 12725785

14. Chibirova OK, Aksenova TI, Benabid A-L, Chabardes S, Larouche S, Rouat J, et al. Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients. Biosystems. 2005;79: 159–171. doi: 10.1016/j.biosystems.2004.09.028 15649601

15. Caro-Martín CR, Delgado-García JM, Gruart A, Sánchez-Campusano R. Spike sorting based on shape, phase, and distribution features, and K-TOPS clustering with validity and error indices. Sci Rep. 2018;8: 17796. doi: 10.1038/s41598-018-35491-4 30542106

16. Fournier J, Mueller CM, Shein-Idelson M, Hemberger M, Laurent G. Consensus-Based Sorting of Neuronal Spike Waveforms. PLoS One. 2016;11: e0160494. Available: https://doi.org/10.1371/journal.pone.0160494

17. Takekawa T, Isomura Y, Fukai T. Accurate spike sorting for multi-unit recordings. Eur J Neurosci. 2010;31: 263–272. doi: 10.1111/j.1460-9568.2009.07068.x 20074217

18. Takekawa T, Isomura Y, Fukai T. Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes. Front Neuroinform. 2012;6: 5. doi: 10.3389/fninf.2012.00005 22448159

19. Pillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP. A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings. PLoS One. 2013;8: 1–14. doi: 10.1371/journal.pone.0062123 23671583

20. Pachitariu M, Steinmetz N, Kadir S, Carandini M, Kenneth D. H. Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels. bioRxiv. 2016; 61481. doi: 10.1101/061481

21. Yger P, Spampinato GLB, Esposito E, Lefebvre B, Deny S, Gardella C, et al. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. Elife. 2018;7: e34518. doi: 10.7554/eLife.34518 29557782

22. Mohammadi Z, Klug A, Liu C, Lei TC. Data reduction for real-time enhanced growing neural gas spike sorting with multiple recording channels. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). 2019. pp. 1084–1087. doi: 10.1109/NER.2019.8717062

23. Rossant C, Kadir SN, Goodman DFM, Schulman J, Hunter MLD, Saleem AB, et al. Spike sorting for large, dense electrode arrays. Nat Neurosci. 2016;19: 634. Available: doi: 10.1038/nn.4268 26974951

24. Jun JJ, Mitelut C, Lai C, Gratiy SL, Anastassiou CA, Harris TD. Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction. bioRxiv. 2017; 101030. doi: 10.1101/101030

25. Edward ES, Kouzani AZ, Tye SJ. Towards miniaturized closed-loop optogenetic stimulation devices. J Neural Eng. 2018;15: 21002. doi: 10.1088/1741-2552/aa7d62 29363618

26. Grosenick L, Marshel JH, Deisseroth K. Closed-loop and activity-guided optogenetic control. Neuron. 2015. pp. 106–139. doi: 10.1016/j.neuron.2015.03.034 25856490

27. Newman JP, Fong M, Millard DC, Whitmire CJ, Stanley GB, Potter SM. Optogenetic feedback control of neural activity. Elife. 2015;4. doi: 10.7554/eLife.07192 26140329

28. Al-Juboori SI, Dondzillo A, Stubblefield EA, Felsen G, Lei TC, Klug A. Light scattering properties vary across different regions of the adult mouse brain. PLoS One. 2013;8. doi: 10.1371/journal.pone.0067626 23874433

29. Deisseroth K. Optogenetics. Nat Methods. 2010;8: 26. Available: doi: 10.1038/nmeth.f.324 21191368

30. Yechao Han, Feiqiang Ma, Hongbao Li, Yueming Wang, Kedi Xu. Optogenetic control of thalamus as a tool for interrupting penicillin induced seizures. Conf Proc. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf. 2015;2015: 6606–9. Available: http://www.ncbi.nlm.nih.gov/pubmed/26737807

31. Wang J, Niebur E, Hu J, Li X. Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller. Sci Rep. 2016;6: 27344. doi: 10.1038/srep27344 27273563

32. Olsson RH, Wise KD. A three-dimensional neural recording microsystem with implantable data compression circuitry. IEEE J Solid-State Circuits. 2005;40: 2796–2804. doi: 10.1109/JSSC.2005.858479

33. Chae MS, Yang Z, Yuce MR, Hoang L, Liu W. A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter. IEEE Trans Neural Syst Rehabil Eng. 2009;17: 312–321. doi: 10.1109/TNSRE.2009.2021607 19435684

34. Rutishauser U, Schuman EM, Mamelak AN. Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo. J Neurosci Methods. 2006;154: 204–224. doi: 10.1016/j.jneumeth.2005.12.033 16488479

35. Karkare V, Gibson S, Markovic D. A 130uW, 64-Channel neural spike-sorting DSP chip. IEEE J Solid-State Circuits. 2011;46: 1214–1222. doi: 10.1109/JSSC.2011.2116410

36. Vaibhav K, Gibson S, Marković D. A 75- uW, 16-channel neural spike-sorting processor with unsupervised clustering. IEEE J Solid-State Circuits. 2013;48: 2230–2238. doi: 10.1109/JSSC.2013.2264616

37. Gibson S, Judy JW, Marković D. An FPGA-based platform for accelerated offline spike sorting. J Neurosci Methods. 2013;215: 1–11. doi: 10.1016/j.jneumeth.2013.01.026 23415852

38. Park J, Kim G, Jung SD. A 128-channel FPGA based real-time spike-sorting bidirectional closed-loop neural interface system. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017. p. 1. doi: 10.1109/TNSRE.2016.2573318

39. Franke F, Quian Quiroga R, Hierlemann A, Obermayer K. Bayes optimal template matching for spike sorting—combining fisher discriminant analysis with optimal filtering. J Comput Neurosci. 2015;38: 439–459. doi: 10.1007/s10827-015-0547-7 25652689

40. Dragas J, Jackel D, Hierlemann A, Franke F. Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm. IEEE Trans Neural Syst Rehabil Eng. 2015;23: 149–158. doi: 10.1109/TNSRE.2014.2370510 25415989

41. Wouters J, Kloosterman F, Bertrand A. Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes. J Neural Eng. 2018;15: 56005. doi: 10.1088/1741-2552/aace8a 29932426

42. Quiroga RQ. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 2004;1687: 1661–1687.

43. Navajas J, Barsakcioglu DY, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga R. Minimum requirements for accurate and efficient real-time on-chip spike sorting. J Neurosci Methods. 2014;230: 51–64. doi: 10.1016/j.jneumeth.2014.04.018 24769170

44. Mukhopadhyay S, Ray GC. A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Transactions on Biomedical Engineering. 1998. pp. 180–187. doi: 10.1109/10.661266 9473841

45. Kim KH, Kim SJ. Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier. IEEE Transactions on Biomedical Engineering. 2000. pp. 1406–1411. doi: 10.1109/10.871415 11059176

46. Gibson S, Judy JW, Marković D. Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction. IEEE Trans Neural Syst Rehabil Eng. 2010;18: 469–478. doi: 10.1109/TNSRE.2010.2051683 20525534

47. Wu H, Zhang J, Huang K, Mallat SG, Feng J, Liu T, et al. Peak detection on ChIP-Seq data using wavelet transformation. IEEE Int Conf Bioinforma Biomed Work. 2010;11: 555–560. doi: 10.1109/34.192463

48. Kim KH, Kim SJ. A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio. IEEE Trans Biomed Eng. 2003;50: 999–1011. doi: 10.1109/TBME.2003.814523 12892327

49. Bestel R, Daus AW, Thielemann C. A novel automated spike sorting algorithm with adaptable feature extraction. J Neurosci Methods. 2012;211: 168–178. doi: 10.1016/j.jneumeth.2012.08.015 22951122

50. Schmitzer-Torbert N, Jackson J, Henze D, Harris K, Redish AD. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience. 2005;131: 1–11. doi: 10.1016/j.neuroscience.2004.09.066 15680687

51. Semmlow J. Biosignal and biomedical image processing: MATLAB-based applications. Vasa. CRC Press; 2004. doi: 10.1201/9780203024058

52. Wang J-S, Swendsen RH. Cluster Monte Carlo algorithms. Phys A Stat Mech its Appl. 1990;167: 565–579. doi: 10.1016/0378-4371(90)90275-W

53. Blatt M, Wiseman S, Domany E. Data clustering using a model granular magnet. Neural Comput. 1997;9: 1805–1842. doi: 10.1162/neco.1997.9.8.1805

54. Blatt M, Wiseman S, Domany E. Superparamagnetic clustering of data. Phys Rev Lett. 1996;76: 3251–3254. doi: 10.1103/PhysRevLett.76.3251 10060920

55. Hill DN, Mehta SB, Kleinfeld D. Quality metrics to accompany spike sorting of extracellular signals. J Neurosci. 2011;31: 8699–8705. doi: 10.1523/JNEUROSCI.0971-11.2011 21677152

56. Wolff U. Comparison between cluster Monte Carlo algorithms in the Ising model. Phys Lett B. 1989;228: 379–382. doi: 10.1016/0370-2693(89)91563-3

57. Porwik P, Lisowska A. The Haar-wavelet transform in digital image processing: its status and achievements. Mach Graph Vis. 2004;13: 79–98.

58. Li A, Guthman EM, Doucette WT, Restrepo D. Behavioral Status Influences the Dependence of Odorant-Induced Change in Firing on Prestimulus Firing Rate. J Neurosci. 2017;37: 1835–1852. doi: 10.1523/JNEUROSCI.3132-16.2017 28093474

59. Li A, Gire DH, Restrepo D. ϒ Spike-Field Coherence in a Population of Olfactory Bulb Neurons Differentiates between Odors Irrespective of Associated Outcome. J Neurosci. 2015;35: 5808–5822. doi: 10.1523/JNEUROSCI.4003-14.2015 25855190

60. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12: 2825–2830.

61. Tye KM, Deisseroth K. Optogenetic investigation of neural circuits underlying brain disease in animal models. Nat Rev Neurosci. 2012;13: 251. Available: doi: 10.1038/nrn3171 22430017

62. Tsien JZ. Cre-lox neurogenetics: 20 years of versatile applications in brain research and counting. Front Genet. 2016;7. doi: 10.3389/fgene.2016.00019 26925095

63. Yang Z, Zhao Q, Keefer E, Liu W. Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A, editors. Advances in Neural Information Processing Systems 22. Curran Associates, Inc.; 2009. pp. 2160–2168. Available: http://papers.nips.cc/paper/3695-noise-characterization-modeling-and-reduction-for-in-vivo-neural-recording.pdf

64. Mohammadi Z, Kincaid JM, Pun SH, Klug A, Liu C, Lei TC. Computationally inexpensive enhanced growing neural gas algorithm for real-time adaptive neural spike clustering. J Neural Eng. 2019;16: 56007. doi: 10.1088/1741-2552/ab208c 31071700

65. Anastassiou CA, Buzsaki C, Koch C, Quiroga R, Panzeri S. Biophysics of extracellular spikes. Princ Neural Coding. 2013;15: 146.

66. Obien MEJ, Deligkaris K, Bullmann T, Bakkum DJ, Frey U. Revealing neuronal function through microelectrode array recordings. Front Neurosci. 2015;8: 423. doi: 10.3389/fnins.2014.00423 25610364

67. Zeinolabedin SMA, Do AT, Jeon D, Sylvester D, Kim TT-H. A 128-channel spike sorting processor featuring 0.175 μW and 0.0033 mm2 per channel in 65-nm CMOS. 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits). 2016. pp. 1–2. doi: 10.1109/VLSIC.2016.7573467

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2019 Číslo 11