Receptive fields without spike-triggering

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  MacKe, Jakob H ^a   Zeck, Günther ^b   Bethge, Matthias ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b MAX PLANCK INSTITUTE OF NEUROBIOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; NEURONS;

GENERALISATION; LINEAR RECEPTIVE FIELDS; NEURAL ACTIVITY; ORIENTATION SELECTIVITY; PROPERTY; RECEPTIVE FIELDS; RESPONSE PATTERNS; REVERSE CORRELATIONS; SENSORY NEURONS; SINGLE NEURON;

CORRELATION METHODS;

EID: 85161997574     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. S. Akaho. A kernel method for canonical correlation analys is. In International Meeting of Psychometric Society, Osaka, 2001 .
2. F. R. Bach and M. I. Jordan. Kernel independent component analys is. Journal of Machine Learning Research, 3 : 1 : 48, 2002.
3. G. Chechik, A. Globerson, N. Tishby, and Y. Weis s. Information Bottleneck for Gaus s ian Variables. The Journal of Machine Learning Research, 6: 1 65-1 8 8, 2005.
4. S. Devries and D. Baylor. Mosaic Arrangement of Ganglion Cell Receptive Fields in Rabbit Retina. Journal ofNeurophysiology, 78 (4) : 2048-2060, 1 997. (Pubitemid 27446986)
5. K. Diamantaras and S. Kung. Cros s -correlation neural network models. Signal Processing, IEEE Transactions on, 42( 1 1 ) : 321 8-3223, 1 994.
6. J. Eichhorn, A. Tolias, A. Zien, M. Kus s, C. E. Rasmus sen, J. Wes ton, N. Logothetis, and B. Schölkopf. Prediction on spike data us ing kernel algorithms. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 1 6. MIT Pres s, Cambridge, MA, 2004.
7. K. Fukumizu, F. R. Bach, and A. Gretton. Statis tical cons is tency of kernel canonical correlation analys is. Journal of Machine Learning Research, 2007.
8. T. Hofmann, B. Schölkopf, and A. Smola. Kernel methods in machine learning. Annals ofStatistics (in press), 2007.
9. H. Hotelling. Relations between two sets of variates. Biometrika, 28 : 321 -3 77, 1 93 6.
10. T. Melzer, M. Reiter, and H. Bischof. Nonlinear feature extraction us ing generalized canonical correlation analys is. In Proc. ofInternational Conference on Artificial Neural Networks (ICANN), pages 3 53-3 60, 8 2001 . (Pubitemid 33316983)
11. L. Paninski. Convergence properties of three spike- triggered analys is techniques. Network, 1 4(3 ) : 43 7-64, Aug 2003 . (Pubitemid 38011144)
12. J. W. Pillow, L. Paninski, V. J. Uzzell, E. P. Simoncelli, and E. J. Chichilnisky. Prediction and decoding of retinal ganglion cell responses with a probabilis tic spiking model. J Neurosci, 25(47) : 1 1 003-1 3, 2005. (Pubitemid 41692630)
13. J. W. Pillow and E. P. Simoncelli. Dimens ionality reduction in neural models : an information- theoretic generalization of spike- triggered average and covariance analys is. J Vis, 6(4) : 41 4-28, 2006. (Pubitemid 43731162)
14. M. J. Schnitzer and M. Meister. Multineuronal firing patterns in the signal from eye to brain. Neuron, 3 7(3 ) : 499-51 1, 2003 . (Pubitemid 36183370)
15. O. Schwartz, J. W. Pillow, N. C. Rus t, and E. P. Simoncelli. Spike- triggered neural characterization. J Vis, 6(4) : 484-507, 2006.
16. H. S. Seung and H. Sompolinsky. Simple models for reading neuronal population codes. Proc Natl Acad Sci U S A, 90(22) : 1 0749-53, 1 993 .
17. T. Sharpee, N. Rus t, and W. Bialek. Analyzing neural responses to natural signals : maximally informative dimens ions. Neural Comput, 1 6(2) : 223-50, 2004. (Pubitemid 38074494)
18. H. Sompolinsky, H. Yoon, K. Kang, and M. Shamir. Population coding in neuronal systems with correlated noise. Phys Rev E Stat Nonlin Soft Matter Phys, 64(5 Pt 1 ) : 051 904, 2001 .
19. J. Victor. Spike train metrics. Curr Opin Neurobiol, 1 5(5) : 58 5-92, 2005. (Pubitemid 41394404)
20. H. Wäs s le. Parallel proces s ing in the mammalian retina. Nat Rev Neurosci, 5( 1 0) : 747-57, 2004. (Pubitemid 39336212)
21. G. M. Zeck, Q. Xiao, and R. H. Mas land. The spatial filtering properties of local edge detectors and brisk- sus tained retinal ganglion cells. Eur J Neurosci, 22(8 ) : 201 6-26, 2005. (Pubitemid 41579391)
22. K. Zhang and T. Sejnowski. Neuronal Tuning: To Sharpen or Broaden?, 1 999.