Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference

Advances in Neural Information Processing Systems

Volumn 2, Issue , 2012, Pages 1304-1312

  Wei, Xue Xin ^a,b   Stocker, Alan A ^a,b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ESTIMATIONS; BAYESIAN INFERENCE; CODING PRINCIPLES; LIKELIHOOD FUNCTIONS; ORIENTATION-TUNING; PRIMARY VISUAL CORTEX; PRIOR DISTRIBUTION; SENSORY INFORMATION;

INFERENCE ENGINES; NEURONS;

BAYESIAN NETWORKS;

EID: 84877775430     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. M. Jones, and B. C. Love. Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34, 169-231,2011.
2. D. P. Andrews. Perception of contours in the central fovea. Nature, 205:1218-1220, 1965.
3. A. Tomassini, M. J.Morgam. and J. A. Solomon. Orientation uncertainty reduces perceived obliquity. Vision Res, 50, 541-547, 2010.
4. W. S. Geisler, D. Kersten. Illusions, perception and Bayes. Nature Neuroscience, 5(6):508-510, 2002.
5. M. O. Ernst Perceptual learning: inverting the size-weight illusion. Current Biology, 19:R23-R25, 2009.
6. G. H. Henry, B. Dreher, P. O. Bishop. Orientation specificity of cells in cat striate cortex. J Neurophysiol, 37(6):1394-409,1974.
7. D. Rose, C. Blakemore An analysis of orientation selectivity in the cat's visual cortex. Exp Brain Res., Apr 30;20(1):1-17, 1974.
8. N. V. Swindale. Orientation tuning curves: empirical description and estimation of parameters. Biol Cybern., 78(1):45-56, 1998.
9. R. L. De Valois, E.W. Yund, N. Hepler. The orientation and direction selectivity of cells in macaque visual cortex. Vision Res.,22, 531544,1982.
10. B. Li, M. R. Peterson, R. D. Freeman. The oblique effect: a neural basis in the visual cortex. J. Neurophysiol., 90, 204217, 2003.
11. D. Ganguli and E.P. Simoncelli. Implicit encoding of prior probabilities in optimal neural populations. In Adv. Neural Information Processing Systems NIPS 23, vol. 23:658-666, 2011.
12. M. D. McDonnell, N. G. Stocks. Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate- Coding Neurons and Populations. Phys Rev Lett., 101(5):058103, 2008.
13. H Helmholtz. Treatise on Physiological Optics (transl.). Thoemmes Press, Bristol, U.K., 2000. Original publication 1867.
14. Y. Weiss, E. Simoncelli, and E. Adelson. Motion illusions as optimal percept. Nature Neuroscience, 5(6):598-604, June 2002.
15. D.C. Knill and W. Richards, editors. Perception as Bayesian Inference. Cambridge University Press, 1996.
16. A R Girshick, M S Landy, and E P Simoncelli. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat Neurosci, 14(7):926-932, Jul 2011.
17. M. Jazayeri and M.N. Shadlen. Temporal context calibrates interval timing. Nature Neuroscience, 13(8):914-916, 2010.
18. A.A. Stocker and E.P. Simoncelli. Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, pages 578-585, April 2006.
19. H.B. Barlow. Possible principles underlying the transformation of sensory messages. InW.A. Rosenblith, editor, Sensory Communication, pages 217-234. MIT Press, Cambridge, MA, 1961.
20. D.M. Coppola, H.R. Purves, A.N. McCoy, and D. Purves The distribution of oriented contours in the real world. Proc Natl Acad Sci U S A., 95(7): 4002-4006, 1998.
21. N. Brunel and J.-P. Nadal. Mutual information, Fisher information and population coding. Neural Computation, 10, 7, 1731-1757, 1998.
22. X-X. Wei and A.A. Stocker. Bayesian inference with efficient neural population codes. In Lecture Notes in Computer Science, Artificial Neural Networks and Machine Learning - ICANN 2012, Lausanne, Switzerland, volume 7552, pages 523-530, 2012.
23. A.A. Stocker and E.P. Simoncelli. Sensory adaptation within a Bayesian framework for perception. In Y.Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 1291-1298. MIT Press, Cambridge, MA, 2006. Oral presentation.
24. D.C. Knill. Robust cue integration: A Bayesian model and evidence from cue-conflict studies with stereoscopic and figure cues to slant. Journal of Vision, 7(7):1-24, 2007.
25. Deep Ganguli. Efficient coding and Bayesian inference with neural populations. PhD thesis, Center for Neural Science, New York University, New York, NY, September 2012.
26. B. Fischer. Bayesian estimates from heterogeneous population codes. In Proc. IEEE Intl. Joint Conf. on Neural Networks. IEEE, 2010.