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http://dx.doi.org/10.9718/JBER.2017.38.6.342

Principles and Current Trends of Neural Decoding  

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)
Publication Information
Journal of Biomedical Engineering Research / v.38, no.6, 2017 , pp. 342-351 More about this Journal
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.
Keywords
neural decoding; spike trains; rate decoding; temporal decoding; population decoding; information theory; and machine-learning;
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1 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.
2 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.   DOI
3 M.A. Paradiso, T. Carney and R.D. Freeman, "Cortical processing of hyperacuity tasks," Vision Res, vol. 29, pp. 247- 254, 1989.   DOI
4 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.
5 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.
6 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.   DOI
7 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.
8 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.
9 F. Chollet, Keras, GitHubs, 2015, https://github.com/fchollet/keras
10 D. Kingma and J. Ba, "A method for stochastic optimization," arXiv preprint arXiv:1412698, pp. 2014.
11 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.
12 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.
13 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.
14 S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput, vol. 9, pp. 1735-1780, 1997.   DOI
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 A.P. Georgopoulos, A.B. Schwartz and R.E. Kettner, "Neuronal population coding of movement direction," Science, vol. 233, pp. 1416-1419, 1986.   DOI
20 S. Gerwinn, J. Macke and M. Bethge, "Bayesian population decoding of spiking neurons," Front Comput Neurosci, vol. 3, pp. 21, 2009.
21 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.   DOI
22 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.   DOI
23 F. Rieke, R. Warland, R. De Ruyter Van Steveninck and W. Bialek, Spikes: Exploring the neural code, MIT Press, 1997.
24 E.D. Adrian, "The impulses produced by sensory nerve endings: Part I," J Physiol, vol. 61, pp. 49-72, 1926.   DOI
25 P. Dayan and L.F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems, MIT Press, 2001.
26 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.
27 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.   DOI