The relationship between optimal and biologically plausible decoding of stimulus velocity in the retina

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 26, Issue 11, 2009, Pages

  Lalor, Edmund C ^a   Ahmadian, Yashar ^b   Paninski, Liam ^b  

^a TRINITY COLLEGE DUBLIN   (Ireland)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DECODING; NEURAL NETWORKS; SIMULATORS; VELOCITY;

APRIORI; BAYESIAN; BAYESIAN ESTIMATE; CORRELATED ACTIVITY; ESTIMATION PERFORMANCE; IMAGE INFORMATION; IMAGE VELOCITY; INTENSITY PROFILES; MOTION ENERGY; NEURAL POPULATIONS; OPEN PROBLEMS; POPULATION SPIKES; RECEPTIVE FIELDS; SPATIAL CORRELATION STRUCTURES; SPATIOTEMPORAL RECEPTIVE FIELD; SPIKE TIMING; SPIKE TRAIN; SPIKING MODEL; STANDARD DEVIATION; VELOCITY INFORMATION;

POPULATION STATISTICS;

ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; CYTOLOGY; HISTOLOGY; HUMAN; MOVEMENT PERCEPTION; PHYSIOLOGY; PSYCHOPHYSICS; RETINA; RETINA GANGLION CELL; STATISTICAL MODEL; VISION;

BAYES THEOREM; COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; MOTION PERCEPTION; PSYCHOPHYSICS; RETINA; RETINAL GANGLION CELLS; VISION, OCULAR;

EID: 70449700255     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.26.000B25     Document Type: Article

References (50)

1. M. Meister, L. Lagnado, and D. Baylor, "Concerted signaling by retinal ganglion cells," Science 270, 1207-1210 (1995).
2. S. Nirenberg, S. Carcieri, A. Jacobs, and P. Latham, "Retinal ganglion cells act largely as independent encoders," Nature 411, 698-701 (2002).
3. E. Chichilnisky and R. Kalmar, "Functional asymmetries in ON and OFF ganglion cells of primate retina," J. Neurosci. 22, 2737-2747 (2002).
4. E. Frechette, A. Sher, M. Grivich, D. Petrusca, A. Litke, and E. Chichilnisky, "Fidelity of the ensemble code for visual motion in the primate retina," J. Neurophysiol. 94, 119-135 (2005).
5. E. Schneidman, M. Berry, R. Segev, and W. Bialek, "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature 440, 1007-1012 (2006).
6. J. Shlens, G. Field, J. Gauthier, M. Grivich, D. Petrusca, A. Sher, A. Litke, and E. Chichilnisky, "The structure of multi-neuron firing patterns in primate retina," J. Neurosci. 26, 8254-8266 (2006).
7. J. Pillow, J. Shlens, L. Paninski, A. Sher, A. Litke, E. Chichilnisky, and E. Simoncelli, "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature 454, 995-999 (2008).
8. E. S. Frechette, M. I. Grivich, R. S. Kalmar, A. M. Litke, D. Petrusca, A. Sher, and E. J. Chichilnisky, "Retinal motion signals and limits on speed discrimination," J. Vision 4, 570 (2004).
9. A. Litke, N. Bezayiff, E. Chichilnisky, W. Cunningham, W. Dabrowski, A. Grillo, M. Grivich, P. Grybos, P. Hottowy, S. Kachiguine, R. Kalmar, K. Mathieson, D. Petrusca, M. Rahman, and A. Sher, "What does the eye tell the brain?: Development of a system for the large-scale recording of retinal output activity," IEEE Trans. Nucl. Sci. 51, 1434-1440 (2004).
10. R. Segev, J. Goodhouse, J. Puchalla, and M. Berry, "Recording spikes from a large fraction of the ganglion cells in a retinal patch," Nat. Neurosci. 7, 1154-1161 (2004).
11. D. Knill and W. Richards, eds., Perception as Bayesian Inference (Cambridge Univ. Press, 1996).
12. E. P. Simoncelli, "Distributed analysis and representation of visual motion," Ph. D. thesis (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1993). Also available as MIT Media Laboratory Vision and Modeling Technical Report #209.
13. D. Ascher and N. Grzywacz, "A Bayesian model for the measurement of visual velocity," Vision Res. 40, 3427-3434 (2000).
14. Y. Weiss, E. Simoncelli, and E. Adelson, "Motion illusions as optimal percepts," Nat. Neurosci. 5, 598-604 (2002).
15. E. P. Simoncelli, "Local analysis of visual motion," in The Visual Neurosciences, L. M. Chalupa and J. S. Werner, eds. (MIT Press, 2003), Chap. 109, pp. 1616-1623.
16. A. Stocker and E. Simoncelli, "Noise characteristics and prior expectations in human visual speed perception," Nat. Neurosci. 9, 578-585 (2006).
17. A. E. Welchman, J. M. Lam, and H. H. Bulthoff, "Bayesian motion estimation accounts for a surprising bias in 3D vision," Proc. Natl. Acad. Sci. U. S. A. 105, 12087-12092 (2008).
18. F. Hurlimann, D. Kiper, and M. Carandini, "Testing the Bayesian model of perceived speed," Vision Res. 42, 2253-2257 (2002).
19. P. Thompson, K. Brooks, and S. Hammett, "Speed can go up as well as down at low contrast: Implications for models of motion perception," Vision Res. 46, 782-786 (2005).
20. A. Thiel, M. Greschner, C. Eurich, J. Ammermüller, and J. Kretzberg, "Contribution of individual retinal ganglion cell responses to velocity and acceleration encoding," J. Neurophysiol. 98, 2285-2296 (2007).
21. J. Kretzberg, I. Winzenborg, and A. Thiel, "Bayesian analysis of the encoding of constant and changing stimulus velocities by retinal ganglion cells," presented at Frontiers in Neuroinformatics 2008, Stockholm, September 7-9, 2008.
22. D. Brillinger, "Maximum likelihood analysis of spike trains of interacting nerve cells," Biol. Cybern. 59, 189-200 (1988).
23. P. McCullagh and J. Nelder, Generalized Linear Models (Chapman & Hall, 1989).
24. L. Paninski, "Maximum likelihood estimation of cascade point-process neural encoding models," Network Comput. Neural Syst. 15, 243-262 (2004). (Pubitemid 39586479)
25. W. Truccolo, U. Eden, M. Fellows, J. Donoghue, and E. Brown, "A point process frame-work for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects," J. Neurophysiol. 93, 1074-1089 (2005).
26. L. Paninski, J. Pillow, and J. Lewi, "Statistical models for neural encoding, decoding, and optimal stimulus design," in Computational Neuroscience: Progress in Brain Research, P. Cisek, T. Drew, and J. Kalaska, eds. (Elsevier, 2007).
27. D. Snyder and M. Miller, Random Point Processes in Time and Space (Springer-Verlag, 1991).
28. D. Field, "Relations between the statistics of natural images and the response profiles of cortical cells," J. Opt. Soc. Am. A 4, 2379-2394 (1987).
29. D. H. Brainard, D. R. Williams, and H. Hofer, "Trichromatic reconstruction from the interleaved cone mosaic: Bayesian model and the color appearance of small spots," J. Vision 8, 1-23 (2008).
30. R. Kass and A. Raftery, "Bayes factors," J. Am. Stat. Assoc. 90, 773-795 (1995).
31. E. Brown, L. Frank, D. Tang, M. Quirk, and M. Wilson, "A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells," J. Neurosci. 18, 7411-7425 (1998).
32. W. Bialek and A. Zee, "Coding and computation with neural spike trains," J. Stat. Phys. 59, 103-115 (1990).
33. S. Koyama and S. Shinomoto, "Empirical Bayes interpretations of random point events," J. Phys. A 38, 531-537 (2005).
34. J. Pillow, Y. Ahmadian, and L. Paninski, "Model-based decoding, information estimation, and change-point detection in multi-neuron spike trains," submitted to Neural Comput.
35. Y. Ahmadian, J. Pillow, and L. Paninski, "Efficient Markov chain Monte Carlo methods for decoding neural spike trains," submitted to Neural Comput.
36. E. Adelson and J. Bergen, "Spatiotemporal energy models for the perception of motion," J. Opt. Soc. Am. A 2, 284-99 (1985).
37. E. Chichilnisky and R. Kalmar, "Temporal resolution of ensemble visual motion signals in primate retina," J. Neurosci. 23, 6681-6689 (2003).
38. W. Bialek (Princeton University, bbrinker@princeton.edu) and R. de Ruyter van Steveninck (Indiana University, deruyter@indiana.edu) (personal communication, 2003).
39. V. Perry and A. Cowey, "The ganglion cell and cone distributions in the monkey's retina: implications for central magnification factors," Vision Res. 25, 1795-1810 (1985).
40. S. Ullman, The Interpretation of Visual Motion (MIT Press, 1979).
41. P. Thompson, "Perceived rate of movement depends on contrast," Vision Res. 22, 377-380 (1982).
42. L. Stone and P. Thompson, "Human speed perception is contrast dependent," Vision Res. 32, 1535-1549 (1992).
43. D. C. Bradley and M. S. Goyal, "Velocity computation in the primate visual system," Nat. Rev. Neurosci. 9, 686-695 (2008).
44. M. Potters and W. Bialek, "Statistical mechanics and visual signal processing," J. Phys. I France 4, 1755-1775 (1994).
45. S. McKee, G. Silvermann, and K. Nakayama, "Precise velocity discrimination despite random variations in temporal frequency and contrast," Vision Res. 26, 609-619 (1986).
46. M. Blakemore and R. Snowden, "The effect of contrast upon perceived speed: a general phenomenon?" Perception 28, 33-48 (1999).
47. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C (Cambridge Univ. Press, 1992).
48. N. Shephard and M. Pitt, "Likelihood analysis of non-Gaussian measurement time series," Biometrika 84, 653-667 (1997).
49. R. Davis and G. Rodriguez-Yam, "Estimation for state-space models: an approximate likelihood approach," Stat. Sin. 15, 381-406 (2005).
50. L. Paninski, Y. Ahmadian, D. Ferreira, S. Koyama, K. Rahnama, M. Vidne, J. Vogelstein, and W. Wu, "A new look at state-space models for neural data," J. Comput. Neurosci. (to be published). Epub ahead of print, doi 10.1007/s10827-009-0179-x.