Bayesian Computation in Recurrent Neural Circuits

Neural Computation

Volumn 16, Issue 1, 2004, Pages 1-38

  Rao, Rajesh P N ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTICLE; ARTIFACT; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BRAIN CORTEX; DISCRIMINATION LEARNING; HUMAN; MOVEMENT PERCEPTION; NERVE CELL; NERVE CELL NETWORK; NERVE TRACT; ORIENTATION; PATTERN RECOGNITION; PHOTOSTIMULATION; PHYSIOLOGY; PROBABILITY; REACTION TIME; SYNAPTIC TRANSMISSION; VISUAL FIELD;

ACTION POTENTIALS; ANIMALS; ARTIFACTS; BAYES THEOREM; CEREBRAL CORTEX; DISCRIMINATION LEARNING; HUMAN; HUMANS; MARKOV CHAINS; MOTION PERCEPTION; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; ORIENTATION; PATTERN RECOGNITION, VISUAL; PHOTIC STIMULATION; REACTION TIME; SUPPORT, NON-U.S. GOV'T; SUPPORT, U.S. GOV'T, NON-P.H.S.; SYNAPTIC TRANSMISSION; VISUAL FIELDS;

EID: 0347527070     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/08997660460733976     Document Type: Article

References (71)

1. Anastasio, T. J., Patton, P. E., & Belkacem-Boussaid, K. (2000). Using Bayes' rule to model multisensory enhancement in the superior colliculus. Neural Computation, 12(5), 1165-1187.
2. Anderson, C. H. (1996). Unifying perspectives on neuronal codes and processing. In E. Ludena, P. Vashishta & R. Bishop (Eds.), Proceedings of the XIX International Workshop on Condensed Matter Theories. New York: Nova Science.
3. Anderson, C. H., & Van Essen, D. C. (1994). Neurobiological computational systems. In J. M. Zurada, R. J. Marks II, C. J., Robinson (Eds.), Computational intelligence: Imitating life (pp. 213-222). New York: IEEE Press.
4. Ascher, D., & Grzywacz, N. M. (2000). A Bayesian model for the measurement of visual velocity. Vision Research, 40, 3427-3434.
5. Barlow, H. B. (1969). Pattern recognition and the responses of sensory neurons. Annals of the New York Academy of Sciences, 156, 872-881.
6. Barlow, H. B. (1972). Single units and cognition: A neurone doctrine for perceptual psychology. Perception, 1, 371-394.
7. Bloj, M. G., Kersten, D., & Hurlbert, A. C. (1999). Perception of three-dimensional shape influences colour perception through mutual illumination. Nature, 402, 877-879.
8. Bridle, J. S. (1990). Alpha-Nets: A recurrent "neural" network architecture with a hidden Markov model interpretation. Speech Communication, 9(1), 83-92.
9. Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1992). The analysis of visual motion: A comparison of neuronal and psychophysical performance. Journal of Neuroscience, 12, 4745-4765.
10. Carpenter, R. H. S., & Williams, M. L. L. (1995). Neural computation of log likelihood in control of saccadic eye movements. Nature, 377, 59-62.
11. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.
12. Dayan, P., & Hinton, G. (1996). Varieties of Helmholtz machine. Neural Networks, 9(8), 1385-1403.
13. Dayan, P., Hinton, G., Neal, R., & Zemel, R. (1995). The Helmholtz machine. Neural Computation, 7, 889-904.
14. DeAngelis, G. C., Robson, J. G., Ohzawa, I., & Freeman, R. D. (1992). Organization of suppression in receptive fields of neurons in cat visual cortex. J. Neurophysiol., 68(1), 144-163.
15. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society Series B, 39, 1-38.
16. Deneve, S., Latham, P. E., & Pouget, A. (1999). Reading population codes: A neural implementation of ideal observers. Nature Neuroscience, 2(8), 740-745.
17. Deneve, S. & Pouget, A. (2001). Bayesian estimation by interconnected neural networks (abstract no. 237.11). Society for Neuroscience Abstracts, 27.
18. Eliasmith, C. & Anderson, C. H. (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. Cambridge, MA: MIT Press.
19. Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47.
20. Földiák, P. (1990). Forming sparse representations by local anti-Hebbian learning. Biol. Cybern., 64, 165-170.
21. Freeman, W. T., Haddon, J., & Pasztor, E. C. (2002). Learning motion analysis. In R. P. N. Rao, B. A. Olshausen, & M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 97-115). Cambridge, MA: MIT Press.
22. Glimcher, P. (2002). Decisions, decisions, decisions: Choosing a biological science of choice. Neuron, 36(2), 323-332.
23. Gold, J. I., & Shadlen, M. N. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences, 5(1), 10-16.
24. Hanes, D. P., & Schall, J. D. (1996). Neural control of voluntary movement initiation. Science, 274, 427-430.
25. Hebb, D. O. (1949). The organization of behavior. New York: Wiley
26. Hinton, G. E., & Brown, A. D. (2002). Learning to use spike timing in a restricted Boltzmann machine. In R. P. N. Rao, B. A. Olshausen, & M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 285-296) Cambridge, MA: MIT Press.
27. Hinton, G. E., & Ghahramani, Z. (1997). Generative models for discovering sparse distributed representations. Phil. Trans. Roy. Soc. Lond. B, 352, 1177-1190.
28. Hinton, G., & Sejnowski, T. (1983). Optimal perceptual inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 448-453) New York: IEEE.
29. Hinton, G., & Sejnowski, T. (1986). Learning and relearning in Boltzmann machines. In D. Rumelhart & J. McClelland, (Eds.), Parallel distributed processing (Vol. 1, pp. 282-317). Cambridge, MA: MIT Press.
30. Hoyer, P. O., & Hyvärinen, A. (in press). Interpreting neural response variability as Monte Carlo sampling of the posterior. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems, 15, Cambridge, MA: MIT Press.
31. Jacobs, R. A. (2002). Visual cue integration for depth perception. In R. P. N. Rao, B. A. Olshausen, & M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 61-76). Cambridge, MA: MIT Press.
32. Jordan, M. I., & Weiss, Y. (2002). Graphical models: Probabilistic inference. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks (2nd ed.). Cambridge, MA: MIT Press.
33. Knierim, J., & Van Essen, D. C. (1992). Neural responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology, 67, 961-980.
34. Knill, D. C., & Richards, W. (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.
35. Koch, C. (1999). Biophysics of computation: Information processing in single neurons. New York: Oxford University Press.
36. Koechlin, E., Anton, J. L., & Burnod, Y. (1999). Bayesian inference in populations of cortical neurons: A model of motion integration and segmentation in area MT. Biological Cybernetics, 80, 25-44.
37. Lewicki, M. S., & Sejnowski, T. J. (1997). Bayesian unsupervised learning of higher order structure. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9 (pp. 529-535). Cambridge, MA: MIT Press.
38. Luce, R. D. (1986). Response time: Their role in inferring elementary mental organization. New York: Oxford University Press.
39. Mamassian, P., Landy, M., & Maloney, L.T. (2002). Bayesian modelling of visual perception. In R. P. N. Rao, B. A. Olshausen, & M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 61-76). Cambridge, MA: MIT Press.
40. Mazurek, M. E., Ditterich, J., Palmer, J., & Shadlen, M. N. (2001). Effect of prior probability on behavior and LIP responses in a motion discrimination task (abstract no. 58.11). Society for Neuroscience Abstracts, 27.
41. Mel, B. W. (1993). Synaptic integration in an excitable dendritic tree. J. Neurophysiology, 70(3), 1086-1101.
42. Miller, E. K., Li, L., & Desimone, R. (1991). A neural mechanism for working and recognition memory in inferior temporal cortex. Science, 254, 1377-1379.
43. Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan (Ed.), Learning in graphical models (pp. 355-368). Norwell, MA: Kluwer.
44. Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400, 233-238.
45. Pouget, A., Dayan, P., & Zemel, R. S. (2000). Information processing with population codes. Nature Reviews Neuroscience, 1(2), 125-132.
46. Pouget, A., Zhang, K., Deneve, S., & Latham, P. E. (1998). Statistically efficient estimation using population coding. Neural Computation, 10(2), 373-401.
47. Rabiner, L., & Juang, B. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, 3, 4-16.
48. Rao, R. P. N. (1999). An optimal estimation approach to visual perception and learning. Vision Research, 39(11), 1963-1989.
49. Rao, R. P. N. & Ballard, D. H. (1997). Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation, 9(4), 721-763.
50. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive field effects. Nature Neuroscience, 2(1), 79-87.
51. Rao, R. P. N., Olshausen, B. A., & Lewicki, M. S. (2002). Probabilistic models of the brain: Perception and neural function. Cambridge, MA: MIT Press.
52. Ratcliff, R., Zandt, T. V., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
53. Reddi, B. A., & Carpenter, R. H. (2000). The influence of urgency on decision time. Nature Neuroscience, 3(8), 827-830.
54. Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19, 1736-1753.
55. Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22, 9475-9489.
56. Schall, J. D., & Hanes, D. P. (1998). Neural mechanisms of selection and control of visually guided eye movements. Neural Networks, 11, 1241-1251.
57. Schall, J. D., & Thompson, K. G. (1999). Neural selection and control of visually guided eye movements. Annual Review of Neuroscience, 22, 241-259.
58. Seung, H. S. (1996). How the brain keeps the eyes still. Proceedings of the National Academy of Sciences, 93, 13339-13344.
59. Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86(4), 1916-1936.
60. Simoncelli, E. P. (1993). Distributed representation and analysis of visual motion. Doctoral dissertation, MIT.
61. Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982). Predictive coding: A fresh view of inhibition in the retina. Proc. R. Soc. Lond. B, 216, 427-459.
62. Usher, M., & McClelland, J. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psychological Review, 108, 550-592.
63. Wang, X.-J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends in Neuroscience, 24, 455-463.
64. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955-968.
65. Weiss, Y., & Fleet, D. J. (2002). Velocity likelihoods in biological and machine vision. In R. P. N. Rao, B. A. Olshausen, & M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 77-96). Cambridge, MA: MIT Press.
66. Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5(6), 598-604.
67. Wu, S., Chen, D., Niranjan, M., & Amari, S.-I. (2003). Sequential Bayesian decoding with a population of neurons. Neural Computation, 15, 993-1012.
68. Zemel, R. S., & Dayan, P. (1999). Distributional population codes and multiple motion models. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in neural information processing systems, 11 (pp. 174-180). Cambridge, MA: MIT Press.
69. Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10(2), 403-430.
70. Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998). Interpreting neuronal population activity by reconstruction: A unified framework with application to hippocampal place cells. Journal of Neurophysiology, 79(2), 1017-1044.
71. Zipser, K., Lamme, V. A. F., & Schiller, P. H. (1996). Contextual modulation in primary visual cortex. Journal of Neuroscience, 16(22), 7376-7389.