Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Principles and Current Trends of Neural Decoding

뉴럴 디코딩의 원리와 최신 연구 동향 소개

  • Kim, Kwangsoo (Department of Electronics and Control Engineering, Hanbat National University) ;
  • Ahn, Jungryul (Department of Physiology, Chungbuk National University School of Medicine) ;
  • Cha, Seongkwang (Department of Physiology, Chungbuk National University School of Medicine) ;
  • Koo, Kyo-in (Department of Biomedical Engineering, University of Ulsan) ;
  • Goo, Yong Sook (Department of Physiology, Chungbuk National University School of Medicine)
  • 김광수 (한밭대학교 전자.제어공학과) ;
  • 안정열 (충북대학교 의과대학 생리학교실) ;
  • 차성광 (충북대학교 의과대학 생리학교실) ;
  • 구교인 (울산대학교 의공학과) ;
  • 구용숙 (충북대학교 의과대학 생리학교실)
  • Received : 2017.11.29
  • Accepted : 2017.12.18
  • Published : 2017.12.31
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Abstract

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

뉴럴 디코딩은 뉴론이 발화한 스파이크 트레인으로부터 뉴론에 인가된 원 자극을 추정하는 작업을 말한다. 디코딩은 뉴론들끼리 어떻게 신호를 주고 받는 지를 이해함으로써 궁극적으로 뇌가 어떻게 정보처리를 하는 지 이해하는 기초적인 작업이다. 이 논문에서 우리는 3가지 뉴럴 디코딩 방법, 즉 빈도 디코딩, 시간 디코딩, 군집 디코딩 방법에 대해 설명하겠다. 빈도 디코딩은 자극에 대한 스파이크의 발화빈도 정보를 이용하여 자극을 복원하는 방법을 말한다. 역사적으로 가장 먼저 시도되었고 가장 간단한 디코딩 방법이다. 그러나 정수 개인 스파이크 개수로부터 빈도를 계산하는 과정에서 빈도자체가 불연속이고 양자화될 가능성이 높기 때문에 간단하고 정적인 자극이 아닌 경우 빈도 디코딩으로는 자극을 복원하기 어렵다는 한계를 가지고 있다. 시간 디코딩은 스파이크 발생 빈도가 아닌 개별 스파이크들의 발생시각을 이용한 디코딩 방법을 말하며 실제 빠르게 변화하는 자극의 경우 신경세포는 빈도 디코딩이 아니라 시간 디코딩을 통해 자극을 추정하는 것으로 이해되고 있다. 군집 디코딩은 단일 신경세포가 아닌 군집 신경세포로부터 자극을 복원하는 방법이다. 군집 디코딩은 단일 신경 세포 디코딩에 비해 신경 세포의 가변성에 따른 불확실성을 감소시킬 수 있고 서로 다른 자극의 특성을 동시에 표현할 수 있다는 장점을 갖는다. 이 논문에서는 먼저 세 가지 뉴럴디코딩 방법에 대해 소개하고 정보이론이 뉴럴디코딩에 어떻게 적용되는 지를 다룬 후 마지막으로 최근에 각광받고 있는 기계학습 방법에 의한 뉴럴 디코딩에 대해 다루도록 하겠다.

Keywords

References

  1. F. Rieke, R. Warland, R. De Ruyter Van Steveninck and W. Bialek, Spikes: Exploring the neural code, MIT Press, 1997.
  2. E.D. Adrian, "The impulses produced by sensory nerve endings: Part I," J Physiol, vol. 61, pp. 49-72, 1926. https://doi.org/10.1113/jphysiol.1926.sp002273
  3. P. Dayan and L.F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems, MIT Press, 2001.
  4. K.H. Britten, M.N. Shadlen, W.T. Newsome and J.A. Movshon, "The analysis of visual motion: a comparison of neuronal and psychophysical performance," J Neurosci, vol. 12, pp. 4745-4765, 1992. https://doi.org/10.1523/JNEUROSCI.12-12-04745.1992
  5. R. De Ruyter Van Steveninck and W. Bialek, "Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences," Proceedings of the Royal Society of London B, vol. 234, pp. 379-414, 1988.
  6. F.E. Theunissen and J.P. Miller, "Representation of sensory information in the cricket cercal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons," J Neurophysiol, vol. 66, pp. 1690-1703, 1991. https://doi.org/10.1152/jn.1991.66.5.1690
  7. J.F. Kalaska, R. Caminiti and A.P. Georgopoulos, "Cortical mechanisms related to the direction of two-dimensional arm movements: relations in parietal area 5 and comparison with motor cortex," Exp Brain Res, vol. 51, pp. 247-260, 1991.
  8. K. Zhang, I. Ginzburg, B.L. Mcnaughton and T.J. Sejnowski, "Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells," J Neurophysiol, vol. 79, pp. 1017-1044, 1998. https://doi.org/10.1152/jn.1998.79.2.1017
  9. M.A. Paradiso, T. Carney and R.D. Freeman, "Cortical processing of hyperacuity tasks," Vision Res, vol. 29, pp. 247- 254, 1989. https://doi.org/10.1016/0042-6989(89)90128-4
  10. I.J. Glaser, H.R. Chowdhury, M.G. Perich, L.E. Miller and K.P. Kording, "Machine learning for neural decoding," arXiv:1708.00909 [q-bio.NC], pp. 2017.
  11. W. Wu, M.J. Black, Y. Gao, M. Serruya, A. Shaikhouni and J. Donoghue, "Neural decoding of cursor motion using a Kalman filter," Advances in neural information processing systems, vol. 15, pp. 1-8, 2003.
  12. E.A. Pohlmeyer, S.A. Solla, E.J. Perreault and L.E. Miller, "Prediction of upper limb muscle activity from motor cortical discharge during reaching," J Neural Eng, vol. 4, pp. 369- 379, 2007. https://doi.org/10.1088/1741-2560/4/4/003
  13. C.C. Chang and C.J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 27, 2011.
  14. T. Chen and C. Guestrin, "Xgboost: A scalable tree boosting system," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
  15. F. Chollet, Keras, GitHubs, 2015, https://github.com/fchollet/keras
  16. D. Kingma and J. Ba, "A method for stochastic optimization," arXiv preprint arXiv:1412698, pp. 2014.
  17. N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014.
  18. T. Tieleman and G. Hinton, Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, vol. 4, pp. 26-31, 2012.
  19. K. Cho, B.V. MerriëNboer, C. Gulcehre, D. Bahdanau, F. Bougares and H. Schwenk, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:14061078, pp. 2014.
  20. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput, vol. 9, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  21. J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E. O'doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez and M.A. Nicolelis, "Learning to control a brain-machine interface for reaching and grasping by primates," PLoS Biol, vol. 1, pp. E42, 2003. https://doi.org/10.1371/journal.pbio.0000042
  22. L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn and J.P. Donoghue, "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, vol. 442, pp. 164-171, 2006. https://doi.org/10.1038/nature04970
  23. S. Panzeri, R.A. Ince, M.E. Diamond and C. Kayser, "Reading spike timing without a clock: intrinsic decoding of spike trains," Philos Trans R Soc Lond B Biol Sci, vol. 369, pp. 20120467, 2014. https://doi.org/10.1098/rstb.2012.0467
  24. A.L. Jacobs, G. Fridman, R.M. Douglas, N.M. Alam, P.E. Latham, G.T. Prusky and S. Nirenberg, "Ruling out and ruling in neural codes," Proc Natl Acad Sci U S A, vol. 106, pp. 5936-5941, 2009. https://doi.org/10.1073/pnas.0900573106
  25. A.P. Georgopoulos, A.B. Schwartz and R.E. Kettner, "Neuronal population coding of movement direction," Science, vol. 233, pp. 1416-1419, 1986. https://doi.org/10.1126/science.3749885
  26. S. Gerwinn, J. Macke and M. Bethge, "Bayesian population decoding of spiking neurons," Front Comput Neurosci, vol. 3, pp. 21, 2009.
  27. J.W. Pillow, J. Shlens, L. Paninski, A. Sher, A.M. Litke, E.J. Chichilnisky and E.P. Simoncelli, "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, vol. 454, pp. 995-999, 2008. https://doi.org/10.1038/nature07140