Skip to main content
SpringerLink
Log in

Big Data Management in Neural Implants: The Neuromorphic Approach

  • Chapter
  • First Online:
Emerging Technology and Architecture for Big-data Analytics

Abstract

In this chapter, we study the brain as a source of ‘big data’ and show how this impedes the scalability of implantable brain machine interfaces for neuroprostheses. The tight power constraints of these systems prevent wireless data transmission of thousands of channels of neural activity. Hence extracting information from the raw data and transmitting just the compressed information is necessary for future implants. This chapter explores several ‘neuromorphic’ solutions to extracting relevant information—spike detection to extract action potentials from raw data, spike sorting to classify the shapes of the action potentials and finally intention decoding to classify spatio-temporal spike trains into categories. We show that using these schemes implies more processing in the implant but can provide compression factors from 10–105. Lastly, a neuromorphic mixed-signal circuit to do intention decoding and provide maximum compression while dissipating sub-μW power is shown as a possible solution for neural implants of the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J. Wessberg, C. Stambaugh, J. Kralik, P. Beck, M. Laubach, J. Chapin, J. Kim, J. Biggs, M. Srinivasan, M. Nicolelis, Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000)

    Article  Google Scholar 

  2. L. Hochberg, M. Serruya, G. Friehs, J. Mukand, M. Saleh, A. Caplan, A. Branner, D. Chen, R. Penn, J. Donoghue, Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006)

    Article  Google Scholar 

  3. L. Hochberg, D. Bacher, B. Jarosiewicz, N. Masse, J. Simeral, J. Vogel, S. Haddain, J. Liu, S. Cash, P. der Smagt, J. Donoghue, Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012)

    Article  Google Scholar 

  4. M. Lebedev, M. Nicolelis, Toward a whole-body neuroprosthetic. Prog. Brain Res. 194, 47–60 (2011)

    Article  Google Scholar 

  5. M. Lebedev, M. Nicolelis, Brain-machine interfaces: past, present and future. Trends Neurosci. 29 (9), 536–546 (2006)

    Article  Google Scholar 

  6. A. Kübler, B. Kotchoubey, J. Kaiser, J. Wolpaw, N. Birbaumer, Brain-computer communication: unlocking the locked in, American Psychological Association, Washington, DC, 2001

    Google Scholar 

  7. Human brain project official website https://www.humanbrainproject.eu/

  8. The BRAIN initiative, NIH website http://www.nih.gov/science/brain/

  9. K. Micheva, B. Busse, N. Weileer, N. O’Rourke, S. Simith, Single-synapse analysis of a diverse synapse population: proteomic imaging methods and markers. Neuron 68, 639–653 (2010)

    Article  Google Scholar 

  10. G. Buzsaki, K. Mizuseki, The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci. 15, 264–78 (2014)

    Article  Google Scholar 

  11. T.M. Seese, H. Harasaki, G.M. Saidel, C. Davies, Characterization of tissue morphology, angiogenesis, and temperature in the adaptive response of muscle tissue in chronic heating. Lab. Invest. 78, 1553–1562 (1998)

    Google Scholar 

  12. S. Kim, R. Normann, R. Harrison, F. Solzbacher, Preliminary study of the thermal impact of a microelectrode array implanted in the brain, in Proceedings of IEEE Engineering in Medicine and Biology Conference (2006), pp. 2986–2989

    Google Scholar 

  13. A. Usakli, Improvement of EEG signal acquisition: an electrical aspect for state of the art of front end. Comput. Intell. Neurosci. 2010, 630649 (2010)

    Google Scholar 

  14. P. Konrad, T. Shanks, Implantable brain computer interface: challenges to neurotechnology translation. Neurobiol. Dis. 38, 369–375 (2010)

    Article  Google Scholar 

  15. A. Hoogerwerf, K. Wise, A three-dimentional microelectrode array for chronic neural recording. IEEE Trans. Biomed. Eng. 41 (12), 1136–1146 (1994)

    Article  Google Scholar 

  16. C.T. Nordhausen, E.M. Maynard, R.A. Normann, Single unit recording capabilities of a 100 microelectrode array. Brain Res. 726, 129–140 (1996)

    Article  Google Scholar 

  17. A.L. Owens, T.J. Denison, H. Versnel, M. Rebbert, M. Peckerar, S.A. Shamma, Multi-electrode array for measuring evoked potentials from the surface of ferret primary auditory cortex. J. Neurosci. Methods 58, 209–220 (1995)

    Article  Google Scholar 

  18. V. Aggarwal, M. Mollazadeh, A.G. Davidson, M.H. Schieber, N.V. Thakor, State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. J. Neurophysiol. 109 (12), 3067–3081 (2013)

    Article  Google Scholar 

  19. K. Rupp, M. Schieber, N.V. Thakor, Local field potentials mitigate decline in motor decoding performance caused by loss of spiking units, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, Aug 2014, pp. 1298–1301

    Google Scholar 

  20. V. Aggarwal, S. Acharya, F. Tenore, H. Shin, R. Etienne-Cummings, M. Schieber, N. Thakor, Asynchronous decoding of dexterous finger movements using M1 neurons. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 3–14 (2008)

    Article  Google Scholar 

  21. S. Acharya, F. Tenore, V. Aggarwal, R. Etienne-Cummings, M. Schieber, and N. Thakor, Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 15–23 (2008)

    Article  Google Scholar 

  22. M. Velliste, S. Perel, M. Spalding, A. Whitford, A. Schwartz, Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101 (2008)

    Article  Google Scholar 

  23. V. Gilja, P. Nuyujukian, C.A. Chestek, J.P. Cunningham, B.M. Yu, J.M. Fan, M.M. Churchland, M.T. Kaufman, J.C. Kao, S.I. Ryu, K.V. Shenoy, A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012)

    Article  Google Scholar 

  24. R. Harrison, The design of integrated circuits to observe brain activity. Proc. IEEE 96 (7), 1203–1216 (2008)

    Article  Google Scholar 

  25. Y. Chen, A. Basu, L. Liu, X. Zou, R. Rajkumar, G.S. Dawe, M. Je, A digitally assisted, signal folding neural recording amplifier. IEEE Trans. Biomed. Circuits Syst. 8 (4), 528–542 (2014)

    Article  Google Scholar 

  26. R. Harrison, A low-power integrated circuit for adaptive detection of action potentials in noisy signals, in Proceeding of the 25th Annual International Conference of the IEEE EMBS (2003)

    Google Scholar 

  27. W. Wattanapanitch, M. Fee, R. Sarpeshkar, An energy-efficient micropower neural recording amplifier. IEEE Trans. Biomed. Circuits Syst. 1 (2), 136–147 (2007)

    Article  Google Scholar 

  28. R. Ginosar, Y. Perelman, Analog frontend for multichannel neuronal recording system with spike and LFP separation. J. Neurosci. Methods 153, 21–26 (2006)

    Article  Google Scholar 

  29. F. Shahrokhi, K. Abdelhalim, D. Serletis, P.L. Carlen, R. Genov, The 128-channel fully differential digital integrated neural recording and stimulation interface. IEEE Trans. Biomed. Circuits Syst. 4 (3), 149–161 (2010)

    Article  Google Scholar 

  30. M. Mollazadeh, K. Murari, G. Cauwenberghs, N. Thakor, Micropower CMOS integrated low-noise amplification, filtering, and digitization of multimodal neuropotentials. IEEE Trans. Biomed. Circuits Syst. 3, 1–10 (2009)

    Article  Google Scholar 

  31. W. Wattanapanitch, R. Sarpeshkar, A low-power 32-channel digitally programmable neural recording integrated circuit. IEEE Trans. Biomed. Circuits Syst. 5, 592–602 (2011)

    Article  Google Scholar 

  32. R.R. Harrison, P.T. Watkins, R.J. Kier, R.O. Lovejoy, D.J. Black, B. Greger, F. Solzbacher, A low-power integrated circuits for a wireless 100-electrode neural recording system. IEEE J. Solid State Circuits 42 (1), 123–133 (2007)

    Article  Google Scholar 

  33. M. Yin, D.A. Borton, J. Aceros, W.R. Patterson, A.V. Nurmikko, A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications. IEEE Trans. Biomed. Circuits Syst. 7 (2), 115–128 (2013)

    Article  Google Scholar 

  34. J. Tan, W.S. Liu, C.H. Heng, Y. Lian, A 2.4 GHz ULP reconfigurable asymmetric transceiver for single-chip wireless neural recording IC. IEEE Trans. Biomed. Circuits Syst. 8 (4), 497–509 (2014)

    Google Scholar 

  35. S.X. Diao, Y.J. Zheng, Y. Gao, S.J. Cheng, X.J. Yuan, M.Y. Je, A 50-Mb/s CMOS QPSK/O-QPSK transmitter employing injection locking for direct modulation. IEEE Trans. Microwave Theory Tech. 60 (1), 120–130 (2012)

    Article  Google Scholar 

  36. M. Chae, Z. Yang, M. Yuce, L. Hoang, W. Liu, A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter. IEEE Trans. Neural Syst. Rehabil. Eng. 17 (4), 312–321 (2009)

    Article  Google Scholar 

  37. C. Mead, Neuromorphic electronic systems. IEEE Proc. 78 (10), 1629–1636 (1990)

    Article  Google Scholar 

  38. R. Sarpeshkar, Efficient precise computation with noisy components: extrapolating from an electronic cochlea to the brain. PhD thesis, California Institute of Technology, Pasadena, CA (1997)

    Google Scholar 

  39. P. Lichtsteiner, C. Posch, T. Delbruck, A 128 × 128 120dB 15us latency asynchronous temporal contrast vision sensor. IEEE J. Solid State Circuits 43 (2), 566–576 (2008)

    Article  Google Scholar 

  40. V. Chan, S.-C. Liu, A. van Schaik, AER EAR: a matched silicon cochlea pair with address event representation interface. IEEE Trans. Circuits Syst. I 54 (1), 48–59 (2007)

    Article  Google Scholar 

  41. G. Indiveri, E. Chicca, R. Douglas, A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17 (1), 211–221 (2006)

    Article  Google Scholar 

  42. G. Indiveri, E. Chicca, R.J. Douglas, Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cogn. Comput. 1, 119–127 (2009)

    Article  Google Scholar 

  43. S. Brink, S. Nease, P. Hasler, S. Ramakrishnan, R. Wunderlich, A. Basu, B. Degnan, A learning-enabled neuron array IC based upon transistor channel models of biological phenomenon. IEEE Trans. Biomed. Circuits Syst. 7 (1), 71–81 (2013)

    Article  Google Scholar 

  44. B.V Benjamin, P. Gao, E. McQuinn, S. Choudhary, A.R. Chandrasekaran, J.-M. Bussat, R. Alvarez-Icaza, J.V. Arthur, P.A. Merolla, K. Boahen, Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102 (5), 699–716 (2014)

    Google Scholar 

  45. K. Boahen, Point-to-point connectivity between neuromorphic chips using address events. IEEE Trans. Circuits Syst. II 47 (5), 416–434 (2000)

    Article  MATH  Google Scholar 

  46. S. Furber, F. Galluppi, S. Temple, L. Plana, The SpiNNaker project. Proc. IEEE 102 (5), 652–665 (2014)

    Article  Google Scholar 

  47. Y. Enyi, C. Yi, A. Basu, A 0.7 V, 40 nW compact, current-mode neural spike detector in 65 nm CMOS. IEEE Trans. Biomed. Circuits Syst. 10 (2), 309–318 (2016)

    Google Scholar 

  48. J. Holleman, A. Mishra, C. Diorio, B. Otis, A micro-power neural spike detector and feature extractor in.13 μm CMOS, in Proceedings of the IEEE Custom Integrated Circuits Conference, Sept 2008, pp. 333–336

    Google Scholar 

  49. E. Koutsos, S.E. Paraskevopoulou, T.G. Constandinou, A 1.5 uW NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces, in Proceedings of the International Symposium on Circuits and Systems, May 2013, pp. 1922–1925

    Google Scholar 

  50. Y.-G. Li, Q. Ma, M.R. Haider, Y. Massoud, Ultra-low-power high sensitivity spike detectors based on modified nonlinear energy operator, in Proceedings of the International Symposium on Circuits and Systems, May 2013, pp. 137–140

    Google Scholar 

  51. L. Liu, L. Yao, X. Zou, W.L. Goh, M. Je, Neural recording front-end IC using action potential detection and analog buffer with digital delay for data compression, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, July 2013, pp. 747–750

    Google Scholar 

  52. Y. Perelman, R. Ginosar, An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection. IEEE Trans. Biomed. Eng. 54 (1), 130–137 (2007)

    Article  Google Scholar 

  53. R.H. Olsson, K.D. Wise, A three-dimensional neural recording microsystem with implantable data compression circuitry. IEEE J. Solid State Circuits 40 (12), 2796–2804 (2016)

    Article  Google Scholar 

  54. T. Horiuchi, T. Swindell, D. Sander, P. Abshire, A low-power CMOS neural amplifier with amplitude measurement for spike sorting, in Proceedings of the 2004 International Symposium on Circuits and Systems, vol. 4 (2004), pp. 23–26

    Google Scholar 

  55. T. Horiuchi, D. Tucker, K. Boyle, P. Abshire, Spike discrimination using amplitude measurements with a low-power CMOS neural amplifier, in IEEE International Symposium on Circuits and Systems (ISCAS) (2007)

    Google Scholar 

  56. A. Bhaduri, E. Yao, A. Basu, Pulse-based feature extraction for hardware-efficient neural recording systems, in International Symposium on Circuits and Systems (ISCAS), Montreal, May 2016

    Google Scholar 

  57. R.Q. Quiroga, Spike sorting. Scholarpedia 2 (12), 3583 (2007)

    Google Scholar 

  58. R.Q. Quiroga, Z. Nadasdy, Y. Ben-Shaul, Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16 (8), 1661–1687 (2004)

    Article  MATH  Google Scholar 

  59. E. Stark, M. Abeles, Predicting movement from multiunit activity. J. Neurosci. 27, 8387–8394 (2007)

    Article  Google Scholar 

  60. V. Ventura, Spike train decoding without spike sorting. Neural Comput. 20, 923–963 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  61. S. Gibson, J. Judy, D. Markovic, Spike sorting: the first step in decoding the brain. IEEE Signal Process. Mag. 29, 124–143 (2012)

    Article  Google Scholar 

  62. V. Karkare, S. Gibson, C. Yang, H. Chen, D. Markovic, A 75 uW, 16-channel neural spike-sorting processor with unsupervised clustering, in IEEE Symposium on VLSI Circuits Digest of Technical Papers (2011)

    Google Scholar 

  63. A. Patil, S. Shen, E. Yao, A. Basu, Random projection for spike sorting: decoding neural signals the neural network way, in Biomedical Circuits and Systems (BioCAS), Atlanta, Oct (2015)

    Google Scholar 

  64. V. Karkare, S. Gibson, D. Marković, A 130-W, 64-channel neural spike-sorting DSP chip. IEEE J. Solid State Circuits 46 (5), 1214–1222 (2011)

    Article  Google Scholar 

  65. V. Karkare, S. Gibson, D. Markovic, A 75-μW, 16-channel neural spike-sorting processor with unsupervised clustering. IEEE J. Solid State Circuits 48 (9), 2230–2238 (2013)

    Article  Google Scholar 

  66. A. Georgopoulos, J. Kalaska, R. Caminiti, J. Massey, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motorcortex. J. Neurosci. 2, 1527–1537 (1982)

    Google Scholar 

  67. A. Georgopoulos, J. Kalaska, R. Caminiti, J. Massey, Spatial coding of movement: a hypothesis concerning the coding of movement direction by motor cortical populations. Exp. Brain Res. Suppl. 7, 327–336 (1983)

    Article  Google Scholar 

  68. A. Georgopoulos, A. Schwartz, R. Kettner, Neuronal population coding of movement direction. Science 233, 1357–1440 (1986)

    Article  Google Scholar 

  69. W. Wu, M. Black, D. Mumford, Y. Gao, E. Bienenstock, J. Donoghue, Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Trans. Biomed. Eng. 51, 933–942 (2004)

    Article  Google Scholar 

  70. W. Wu, Y. Gao, E. Bienenstock, J. Donoghue, M. Black, Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput. 18, 80–118 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  71. A. Brockwell, A. Rojas, R. Kass, Recursive bayesian decoding of motor cortical signals by particle filtering. J. Neurophysiol. 91, 1899–1907 (2004)

    Article  Google Scholar 

  72. S. Lin, J. Si, A. Schwartz, Self-organization of firing activities in monkey’s motor cortex: trajectory computation from spike signals. Neural Comput. 9, 607–621 (1997)

    Article  Google Scholar 

  73. B. Rapoport, W. Wattanapanitch, H. Penagos, S. Musallam, R. Andersen, R. Sarpeshkar, A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders, in 31st Annual International Conference of the IEEE EMBS (2009)

    Google Scholar 

  74. B. Rapoport, L. Turicchian, W. Wattanapanitch, T. Davidson, R. Sarpeshkar, Efficient universal computing architectures for decoding neural activity. PLoS ONE 7, e42492 (2012)

    Article  Google Scholar 

  75. J. Dethier, V. Gilja, P. Nuyujukian, S.A. Elassaad, K.V. Shenoy, K. Boahen, Spiking neural network decoder for brain-machine interfaces, in 5th International IEEE/EMBS Conference on Neural Engineering (NER), 2011 (2011)

    Google Scholar 

  76. C. Yi, Y. Enyi, A. Basu, A 128 channel extreme learning machine based neural decoder for brain machine interfaces. IEEE Trans. Biomed. Circuits Syst. 10 (3), 679–692 (2016)

    Article  Google Scholar 

  77. G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machines: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  78. G.-B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern. 42 (2), 513–529 (2012)

    Article  Google Scholar 

  79. P.R. Kinget, Device mismatch and tradeoffs in the design of analog circuits. IEEE J. Solid State Circuits 40 (6), 1212–1224 (2005)

    Article  Google Scholar 

  80. Y. He, C.H. Chang, A new redundant binary booth encoding for fast 2n-bit multiplier design. IEEE Trans. Circuits Syst. I 56 (6), 1192–1201 (2009)

    Article  MathSciNet  Google Scholar 

  81. K.S. Chong, B.H. Gwee, J.S. Chang, A micropower low-voltage multiplier with reduced spurious switching. IEEE Trans. VLSI 13 (2), 255–265 (2005)

    Article  Google Scholar 

  82. M. La Guia de Solaz, R. Conway, Razor based programmable truncated multiply and accumulate, energy-reduction for efficient digital signal processing. IEEE Trans. VLSI 23 (1), 189–193 (2015)

    Article  Google Scholar 

  83. J. DiGiovanna, B. Mahmoudi, J. Fortes, J. Principe, J. Sanchez Co-adaptive brain machine interface via reinforcement learning. IEEE Trans. Biomed. Eng. 54 (64), 56–61 (2009)

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge funding support from NTU and MOE, Mediatek for supporting chip design and Prof. Nitish Thakor for providing neural data from primate experiments.

Author information

Authors and Affiliations

  1. School of EEE, Nanyang Technological University, Singapore, Singapore

    Arindam Basu, Chen Yi & Yao Enyi

Authors
  1. Arindam Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao Enyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arindam Basu .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Anupam Chattopadhyay

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Chip Hong Chang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Hao Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Basu, A., Yi, C., Enyi, Y. (2017). Big Data Management in Neural Implants: The Neuromorphic Approach. In: Chattopadhyay, A., Chang, C., Yu, H. (eds) Emerging Technology and Architecture for Big-data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-54840-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54840-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54839-5

  • Online ISBN: 978-3-319-54840-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation