Hierarchical bayesian inference in networks of spiking neurons

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Rao, Rajesh P N ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; GRAPHIC METHODS; HIERARCHICAL SYSTEMS; INFERENCE ENGINES; NEURONS; PROBABILITY;

APPROXIMATE BAYESIAN INFERENCE; BELIEF PROPAGATION; HIERARCHICAL BAYESIAN; HIERARCHICAL MODEL; HIERARCHICAL NETWORK; INTEGRATE-AND-FIRE NEURONS; MEMBRANE POTENTIALS; POSTERIOR PROBABILITY;

NEURAL NETWORKS;

EID: 84899012554     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. C. H. Anderson and D. C. Van Essen. Neurobiological computational systems. In Computational Intelligence: Imitating Life, pages 213-222. New York, NY: IEEE Press, 1994.
2. R. H. S. Carpenter and M. L. L. Williams. Neural computation of log likelihood in control of saccadic eye movements. Nature, 377:59-62, 1995.
3. S. Deneve and A. Pouget. Bayesian estimation by interconnected neural networks (abstract no. 237.11). Society for Neuroscience Abstracts, 27, 2001.
4. W. Gerstner. Population dynamics of spiking neurons: Fast transients, asynchronous states, and locking. Neural Computation, 12(1):43-89, 2000.
5. J. I. Gold and M. N. Shadlen. Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences, 5(1):10-16, 2001.
6. M. Hausser and B. Mel. Dendrites: bug or feature? Current Opinion in Neurobiology, 13:372- 383, 2003.
7. D. C. Knill and W. Richards. Perception as Bayesian Inference. Cambridge, UK: Cambridge University Press, 1996.
8. K. P. Kording and D.Wolpert. Bayesian integration in sensorimotor learning. Nature, 427:244- 247, 2004.
9. T. S. Lee and D. Mumford. Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20(7):1434-1448, 2003.
10. B. W. Mel. NMDA-based pattern discrimination in a modeled cortical neuron. Neural Computation, 4(4):502-517, 1992.
11. K. Murphy, Y. Weiss, and M. Jordan. Loopy belief propagation for approximate inference: An empirical study. In Proceedings of UAI (Uncertainty in AI), pages 467-475. 1999.
12. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA, 1988.
13. H. E. Plesser and W. Gerstner. Noise in integrate-and-fire neurons: From stochastic input to escape rates. Neural Computation, 12(2):367-384, 2000.
14. A. Pouget, P. Dayan, and R. S. Zemel. Inference and computation with population codes. Annual Review of Neuroscience, 26:381-410, 2003.
15. R. P. N. Rao. Bayesian computation in recurrent neural circuits. Neural Computation, 16(1):1- 38, 2004.
16. R. P. N. Rao, B. A. Olshausen, and M. S. Lewicki. Probabilistic Models of the Brain: Perception and Neural Function. Cambridge, MA: MIT Press, 2002.
17. J. H. Reynolds, L. Chelazzi, and R. Desimone. Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19:1736-1753, 1999.
18. M. Sahani and P. Dayan. Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicity. Neural Computation, 15:2255-2279, 2003.
19. Y. Weiss, E. P. Simoncelli, and E. H. Adelson. Motion illusions as optimal percepts. Nature Neuroscience, 5(6):598-604, 2002.
20. R. S. Zemel, P. Dayan, and A. Pouget. Probabilistic interpretation of population codes. Neural Computation, 10(2):403-430, 1998.