뉴럴 디코딩의 원리와 최신 연구 동향 소개
Principles and Current Trends of Neural Decoding
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (27)
- F. Rieke, "De Ruyter Van Steveninck and W. Bialek, Spikes: Exploring the neural code" (1997), MIT Press
- E. D. Adrian, "The impulses produced by sensory nerve endings : Part I", J Physiol, 61, pp.49~72 (1926)
- P. Dayan, "Theoretical neuroscience: computational and mathematical modeling of neural systems" (2001), MIT Press
- K.H. Britten, "The analysis of visual motion : a comparison of neuronal and psychophysical performance", J Neurosci, 12, pp.4745~4765 (1992)
- R. De Ruyter Van Steveninck, "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, 234, pp.379~414 (1988)
- F.E. Theunissen, "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, 66, pp.1690~1703 (1991)
- J.F. Kalaska, "Cortical mechanisms related to the direction of two-dimensional arm movements : relations in parietal area 5 and comparison with motor cortex", Exp Brain Res, 51, pp.247~260 (1991)
- K. Zhang, "Interpreting neuronal population activity by reconstruction : unified framework with application to hippocampal place cells", J Neurophysiol, 79, pp.1017~1044 (1998)
- M.A. Paradiso, "Cortical processing of hyperacuity tasks", Vision Res, 29, pp.247~254 (1989)
- "2017", ~
- W. Wu, "Neural decoding of cursor motion using a Kalman filter", Advances in neural information processing systems, 15, pp.1~8 (2003)
- E. A. Pohlmeyer, "Prediction of upper limb muscle activity from motor cortical discharge during reaching", J Neural Eng, 4, pp.369~379 (2007)
- C.C. Chang, "LIBSVM : a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2, pp.27~ (2011)
- T. Chen, "Xgboost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785~794 (2016)
- "Keras" (2015)[URL]
- D. Kingma, "A method for stochastic optimization", arXiv preprint arXiv:1412698, ~ (2014)
- N. Srivastava, "Dropout : a simple way to prevent neural networks from overfitting", Journal of Machine Learning Research, 15, pp.1929~1958 (2014)
- T. Tieleman, "Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude", Neural Networks for Machine Learning, 4, pp.26~31 (2012)
- K. Cho, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv preprint arXiv:14061078, ~ (2014)
- S. Hochreiter, "Long short-term memory", Neural Comput, 9, pp.1735~1780 (1997)
- J. M. Carmena, "Learning to control a brain-machine interface for reaching and grasping by primates", PLoS Biol, 1, pp.E42~ (2003)
- L.R. Hochberg, "Neuronal ensemble control of prosthetic devices by a human with tetraplegia", Nature, 442, pp.164~171 (2006)
- S. Panzeri, "Reading spike timing without a clock : intrinsic decoding of spike trains", Philos Trans R Soc Lond B Biol Sci, 369, pp.20120467~ (2014)
- A. L. Jacobs, "Ruling out and ruling in neural codes", Proc Natl Acad Sci U S A, 106, pp.5936~5941 (2009)
- A. P. Georgopoulos, "Neuronal population coding of movement direction", Science, 233, pp.1416~1419 (1986)
- S. Gerwinn, "Bayesian population decoding of spiking neurons", Front Comput Neurosci, 3, pp.21~ (2009)
- J. W. Pillow, "Spatio-temporal correlations and visual signalling in a complete neuronal population", Nature, 454, pp.995~999 (2008)