nips nips2000 nips2000-102 nips2000-102-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhaoping Li, Peter Dayan
Abstract: Stimulus arrays are inevitably presented at different positions on the retina in visual tasks, even those that nominally require fixation. In particular, this applies to many perceptual learning tasks. We show that perceptual inference or discrimination in the face of positional variance has a structurally different quality from inference about fixed position stimuli, involving a particular, quadratic, non-linearity rather than a purely linear discrimination. We show the advantage taking this non-linearity into account has for discrimination, and suggest it as a role for recurrent connections in area VI, by demonstrating the superior discrimination performance of a recurrent network. We propose that learning the feedforward and recurrent neural connections for these tasks corresponds to the fast and slow components of learning observed in perceptual learning tasks.
[1] Karni A and Sagi D. Nature 365 250-252,1993.
[2] Fahle M. Edelman S. and Poggio T. Vision Res. 35 3003-3013, 1995.
[3] Fahle M. Perception 23 411-427, (1994). And also Fahle M. Vis. Res. 37(14) 1885-1895, (1997).
[4] Poggio T. Fahle M. and Edelman S. Science 2561018-1021, 1992.
[5] Weiss Y. Edelman S. and Fahle M. Neural Computation 5 695-718, 1993.
[6] Li, Zhaoping Network: Computation in Neural Systems 10(2) 187-212, 1999.
[7] Pouget A, Zhang K, Deneve S, Latham PE. Neural Comput. 10(2):373-401, 1998.
[8] Seung HS , Sompolinsky H. Proc Natl Acad Sci USA . 90(22):10749-53, 1993
[9] Koch C. Biophysics of computation. Oxford University Press, 1999.
[10] Gilbert C. Presentation at the Neural Dynamics Workshop, Gatsby Unit, 2/2000.
[11] Riesenhuber M, Poggio T. Nat Neurosci. 2(11):1019-25, 1999.
[12] Fukushima, K. BioI. Cybem. 36193-202, 1980.