Bayesian spiking neurons I: Inference

Neural Computation

Volumn 20, Issue 1, 2008, Pages 91-117

  Deneve, Sophie ^a  

^a l'alimentation et l'Environnement   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ADAPTATION; ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CENTRAL NERVOUS SYSTEM; COMPUTER SIMULATION; HUMAN; MOVEMENT (PHYSIOLOGY); NERVE CELL; NERVE CELL NETWORK; PERCEPTION; PHYSIOLOGY; PROBABILITY; STATISTICAL MODEL; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ADAPTATION, PHYSIOLOGICAL; ALGORITHMS; ANIMALS; BAYES THEOREM; CENTRAL NERVOUS SYSTEM; COMPUTER SIMULATION; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MOVEMENT; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS; PERCEPTION; SYNAPTIC TRANSMISSION;

EID: 37749042762     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2008.20.1.91     Document Type: Article

References (41)

1. Agüera y Arcas, B., Fairhall, A., & Bialek, W. (2003). Computation in a single neuron: Hodgkin and Huxley revisited. Neural Computation, 15(8), 1715-1749.
2. Bair, W. (1999). Spike timing in the mammalian visual system. Current Opinion in Neurobiology, 9, 447-453.
3. Ballard, D., Hayhoe, M., Salgian, G., & Shinoda, H. (2000). Spatio-temporal organization of behavior. Spatial Vision, 12(2-3), 321-333.
4. Barber, M., Clark, J., & Anderson, C. (2003). Neural representation of probabilistic information. Neural Computation, 15(8), 1844-1853.
5. 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.
6. Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415, 429-433.
7. Feldman, J. (2001). Bayesian contour integration. Percept. Psychophys., 63, 1171-1182.
8. Frey, B. (1998). Graphical models for machine learning and digital communication. Cambridge, MA: MIT Press.
9. Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41, 711-724.
10. Gerstner, W., & Werner, M. (2002). Spiking neuron models. Cambridge: Cambridge University Press.
11. Ghahramani, Z., & Jordan, M. (1997). Factorial hidden Markov models. Machine Learning, 29, 245-273.
12. Ghahramani, Z., Wolpert, D., & Jordan, M. (1995). Computational structure of coordinate transformations: A generalization study. In G. Tesauro, D. Touretzky, & T. K. Leen (Eds.), Advances in neural information processing systems, 7. San Mateo, CA: Morgan Kaufmann.
13. Glimcher, P., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 15, 306(5695), 447-452.
14. Hinton, G., & Ghahramani, Z. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, 3(1), 4-16.
15. Hinton, G., & Ghahramani, Z. (1997). Generative model for discovering sparse distributed representation. Philos. Trans. R. Soc. Lond. B Biol. Sci., 352(1358), 1177-1190.
16. Hubel, D., & Wiesel, T. (1970). Cells sensitive to binocular depth in area 18 of the macaque monkey cortex. Nature, 225, 41-42.
17. Huys, Q. J., Zemel, R. S., Natarajan, R., & Dayan, P. (2007). Fast population coding. Neural Comput., 19, 404-441.
18. Jordan, M. (1974). Learning in graphical models. Cambridge, MA: MIT Press.
19. Knill, D., & Richards, W. (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.
20. Kording, K., & Wolpert, D. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244-247.
21. Liu, G. (2004). Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nature Neuroscience, 7(4), 373-379.
22. Liu, Y., & Wang, X. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 20(1), 25-45.
23. Mazurek, M., Roitman, J., Ditterich, J., & Shadlen, M. (2003). A role for neural integrators in perceptual decision making. Cerebral Cortex, 23(11), 1257-1269.
24. Rao, R. (2003). Bayesian computation in recurrent neural circuits. Neural Computation, 16(1), 1-38.
25. Reinagel, P., & Reid, R. (2000). Temporal coding of visual information in the thalamus. Journal of Neuroscience, 20(14), 5392-5400.
26. Sahani, M., & Dayan, P. (2003). Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicity. Neural Computation, 25(10), 2255-2279.
27. Shadlen, M., & Newsome, W. (1994). Noise, neural codes and cortical organization. Current Opinion in Neurobiology, 4, 569-579.
28. Shadlen, M., & Newsome, W. (2001). Neural basis of a perceptual decision in the parietal cortex (area lip) of the rhesus monkey. Journal of Neurophysiology, 86(4), 1916-1936.
29. Sugrue, L., Corrado, G., & Newsome, W. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304(5678), 1782-1787.
30. Tao, H., & Poo, M. (2005). Activity-dependent matching of excitatory and inhibitory inputs during refinement of visual receptive fields. Neuron, 45(6), 829-836.
31. Tolhurst, D., Movshon, J., & Dean, A. (1982). The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research, 23, 775-785.
32. van Beers, R., Sittig, A., & Gon, J. (1999). Integration of proprioceptive and visual position-information: An experimentally supported model. Journal of Neurophysiology, 81(3), 1355-1364.
33. Vinje, W., & Gallant, J. (2002). Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1. Journal of Neuroscience, 22(7), 2904-2915.
34. Vogels, R., Spilleers, W., & Orban, G. (1989). The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research, 77, 432-436.
35. Weiss, Y., & Fleet, D. (2002). Velocity likelihood in biological and machine vision. In R. Rao, B. Olshausen, & M. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 77-96). Cambridge, MA: MIT Press.
36. Weiss, Y., & Freeman, W. (2001). Correctness of belief propagation in gaussian graphical models of arbitrary topology. Neural Computation, 13, 2173-2200.
37. Wolpert, D., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience Supplement, 3, 1212-1217.
38. Wu, S., & Amari, S. (2002). Neural implementation of Bayesian inference in population codes. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14. Cambridge, MA: MIT Press.
39. Zemel, R., Dayan, P., & Pouget, A. (1997). Population code representations of probability density functions. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9. Cambridge, MA: MIT Press.
40. Zemel, R., Dayan, P., & Pouget, A. (1998). Probabilistic interpolation of population code. Neural Computation, 10(2), 403-430.
41. Zohary, E., Shadlen, M., & Newsome, W. (1994). Correlated neuronal discharge rate and its implication for psychophysical performance. Nature, 370, 140-143.