Decoding grating orientation from microelectrode array recordings in monkey cortical area V4

International Journal of Neural Systems

Volumn 20, Issue 2, 2010, Pages 95-108

  Manyakov, Nikolay V ^a   Van Hulle, Marc M ^a  

^a KU LEUVEN UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Brain Machine Interface (BMI); Brain signal decoding; Feature selection; Microelectrode array; Multiclass classification; Spike train

Indexed keywords

BRAIN MACHINE INTERFACE; BRAIN SIGNAL DECODING; BRAIN SIGNALS; FEATURE SELECTION; MICROELECTRODE ARRAY; MULTI-CLASS CLASSIFICATION; SPIKE TRAIN;

CLASSIFIERS; DECODING; DISCRIMINANT ANALYSIS; IMAGE RETRIEVAL; MICROELECTRODES; SURFACE DEFECTS;

FEATURE EXTRACTION;

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN MAPPING; COMPUTER INTERFACE; CYTOLOGY; DISCRIMINANT ANALYSIS; MACACA; METHODOLOGY; MICROELECTRODE; ORIENTATION; PHOTOSTIMULATION; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; PYRAMIDAL NERVE CELL; VISUAL CORTEX;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; BRAIN MAPPING; DISCRIMINANT ANALYSIS; MACACA MULATTA; MICROELECTRODES; MODELS, NEUROLOGICAL; ORIENTATION; PATTERN RECOGNITION, AUTOMATED; PHOTIC STIMULATION; PRINCIPAL COMPONENT ANALYSIS; PYRAMIDAL CELLS; USER-COMPUTER INTERFACE; VISUAL CORTEX;

EID: 77951521245     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065710002280     Document Type: Article

References (43)

1. 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, Neural ensemble control of prosthetic devices by human with tetraplegia, Nature 442 (2006) 164-171. (Pubitemid 44086558)
2. G. Santhanam, S. I. Ryu, B. M. Yu, A. Afshar and K. V. Shenoy, A high-performance brain-computer interface, Nature 442 (2006) 195-198. (Pubitemid 44086565)
3. M. A. Nicolelis, Brain-machine interface to restore motor function and probe neural circuits, Nat. Rev. Neuroscience 4 (2003) 417-422.
4. M. Velliste, S. Perel, M. Ch. Spalding, A. S. Whitford and A. B. Schwartz, Cortical control of a prosthetic arm for self-feeding, Nature 453 (2008) 1098-1101. (Pubitemid 351871735)
5. D. H. Hubel and T. N. Wiesel, Receptive fields and functional architecture of monkey striate cortex, J. Physiol. 195 (1968) 215-243.
6. G. G. Blasdel, Orientation selectivity, preference, and continuity in monkey striate cortex, J. Neurosci. 12 (1992) 3139-3161.
7. E. Bartfeld and A. Grinvald, Relationships between orientation-preference pinwheels, cytochrome oxidase blobs, and ocular-dominance columns in primate striate cortex, Proc. Natl. Acad. Sci. USA 89 (1992) 11905-11909. (Pubitemid 23004753)
8. W. Vanduffel, R. B. Tootell, A. A. Schoups and G. A. Orban, The organization of orientation selectivity throughout macaque visual cortex, Cereb. Cortex 12 (2002) 647-662. (Pubitemid 34533655)
9. A. Pouget, P. Dayan and R. Zemel, Information processing with population codes, Nat. Rev. Neurosci. 1 (2000) 125-132.
10. M. A. Paradiso, A theory for the use of visual orientation information which exploits the columnar structure of striate cortex, Biol. Cybern. 58 (1988) 35-49.
11. A. P. Georgopoulos, A. B. Schwarz and R. E. Kettner, Neural population coding of movement direction, Science 233 (1986) 1416-1419.
12. R. Desimone, S. J. Schein, J. Morgan and L. G. Ungerleider, Contour, color and shape analysis beyond the striate cortex, Vision Research 43 (1985) 1035-1045. (Pubitemid 15094585)
13. R. Quian Quiroga, Z. Nadasdy and Y. Ben-Shaul, Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering, Neural Compuation 16 (2004) 1661-1687. (Pubitemid 38835137)
14. K. H. Kim and S. J. Kim, Methods for unsupervised classification of multiunit neural signal recordings under low signal-to-noise ratio, IEEE Trans. Biomed. Eng. 50 (2003) 421-431.
15. M. Abeles and Jr. M. Goldstein, Multispike train analysis, Proceedings of the IEEE 65 (1977) 762-773. (Pubitemid 8260467)
16. I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning research 3 (2003) 1157-1182.
17. S. Abe, Support Vector Machine for Pattern Classification (New York: Springer-Verlag, 2005).
18. J. Luts, A. Heerschap, J. A. K. Suykens and S. Van Huffel, A combined MRI and MRSI based multiclass system for brain tumor recognition using LS-SVMs with class probabilities and feature selection, Artificial Intelligence in Medicine 40 (2007) 87-102. (Pubitemid 46805135)
19. T. Li, Ch. Zhang and M. Ogihara, A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatiics 20 (15) (2004) 2429-2437. (Pubitemid 39429915)
20. G. Forman, An extensive empirical study of feature selection metrics for text classification, Journal of Machine Learning Research 3 (2003) 1289-1306.
21. A. Blum and P. Langley, Selection of relevant features and examples in machine learning, Artificial Intelligence 97 (1-2) (1997) 245-271. (Pubitemid 127401106)
22. R. Kovahi and G. John, Wrappers for feature selection, Artificial Intelligence 97 (1-2) (1997) 273-324.
23. D. Wettschereck, D. W. Aha and T. Mohri, A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms, Artificial Intelligence Review 11 (1997) 273-314. (Pubitemid 127508242)
24. T. G. Dietterich, Machine learning research: Four current directions, AI Magazine 18 (1998) 97-136.
25. M. Robnik-Šikonja and I. Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning 53 (2003) 23-69.
26. V. Vapnik, The Nature of Statistical Learning Theory (Springer-Verlag, 1995).
27. A. G. Ivakhnenko and G. A. Ivakhnenko, The review of problem solvable by algorithms of the group methods of data handling (GMDH), Pattern Recognition and Image Analysis 5 (4) (1995) 527-535.
28. K. Kira and L. A. Rendell, The feature selection problem: Traditional methods and a new algorithm, Proc. of Ninth National Conference on AI (1992) 129-134. (Pubitemid 23633599)
29. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, Prediction (Springer-Verlag, 2001).
30. E. Allwein, R. Schapire and Y. Singer, Reducing multiclass to binary: A unifying approach for margin classifiers, Journal of Machine Learning Research 1 (2000) 113-141. (Pubitemid 33738778)
31. U. H.-G. Kressel, Pairwise classification and support vector machines, in Advance in Kernel Methods - Support Vector Learning (eds.) B. Schölkopf
32. C. Burges and A. J. Smola (The MIT Press, Cambridge, MA, 1999), pp. 255-268.
33. T. G. Dietterich and G. Bakari, Solving multiclass learning problem via error-correcting output codes, Journal of Artificial Intelligent Research 2 (1995) 263-286.
34. B. Schölkopf and A. J. Smola, Learning with a Kernels (MIT Press, Cambridge, MA, 2002).
35. T. Joachims, Making large-Scale SVM Learning Practical, in Advance in Kernel Methods-Support Vector Learning (eds.) B. Schölkopf, C. Burges and A. J. Smola (The MIT Press, Cambridge, MA, 1999), pp. 169-184.
36. J. Platt, Probabilistic outputs for support vector machines and comparison to regularized likelihood methods, in Advances in Large Margin Classifiers (eds.) A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans (MIT Press, Cambridge, MA, 2000), pp. 61-74.
37. S. Knerr, L. Personnaz, and G. Dreyfus, Single-layer learning revisited: A stepwise procedure for building and training a neural network, in Neurocomputing: Algorithms, Architectures and Applications (eds.) F. Fogelman Soulié and J. Hérault (Springer-Verlag, Berlin, 1990), pp. 41-50.
38. D. Price, S. Knerr, L. Personnaz, and G. Dreyfus, Pairwise neural network classifiers with probabilistic outputs, in Neural Information Processing Systems (eds.) G. Tesauro, D. Touretzky and T. Leen (The MIT Press, Cambridge, MA, 1995), pp. 1109-1116.
39. T. Hastie and R. Tibshirani, Classification by pairwise coupling, The Annals of Statistics 26 (1) (1998) 451-471. (Pubitemid 128089819)
40. T.-F. Wu, Ch.-J. Li and R. C. Weng, Probability estimates for multi-class classification by pairwise coupling, Journal of Machine Learning Research 5 (2004) 975-1005.
41. G. W. Brier, Verification of forecasts expressed in probabilities, Monthly Weather Review 78 (1) (1950) 1-3.
42. G. W. Corder and D. I. Foreman, Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach (New York, J. Wiley, 2009).
43. Y. Kamitani and F. Tong, Decoding the visual and subjective contents of the human brain, Nature Neuroscience 8 (5) (2005) 679-685. (Pubitemid 40594211)