Information transmission with spiking Bayesian neurons

New Journal of Physics

Volumn 10, Issue , 2008, Pages

  Lochmann, Timm ^a   Denève, Sophie ^a  

^a ECOLE NORMALE SUPÉRIEURE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; LOCOMOTIVES; NEURONS; POISSON DISTRIBUTION; POISSON EQUATION; PROGRAMMING THEORY; RAILROAD CARS; STANDARDS; STATISTICAL METHODS; STATISTICS; STOCHASTIC MODELS; STOCHASTIC PROGRAMMING; TIME VARYING SYSTEMS;

BAYESIAN; BOTTOM-UP; CORTICAL NEURONS; CURRENT STATE; FIRING RATES; INFORMATION TRANSFERRING; INFORMATION TRANSMISSION; INPUT AND OUTPUT (I/O); INTEGRATE AND FIRE (AIF); NEURAL CODING; OUTPUT VARIABILITY; POISSON; POTENTIAL EFFECTS; SENSORY INFORMATION; SENSORY NEURONS; SPIKE GENERATION; SPIKE TRAINS; SPIKING NEURONS; STANDARD MODEL (SM); TIME VARYING; TIME-VARYING; TOP-DOWN;

MILITARY OPERATIONS;

EID: 46749086291     PISSN: 13672630     EISSN: None     Source Type: Journal    
DOI: 10.1088/1367-2630/10/5/055019     Document Type: Article

References (49)

1. Tolhurst D, Movshon J and Dean A 1983 The statistical reliability of signals in single neurons in cat and monkey visual cortex Vision Res. 23 775-85
2. Shannon C and Weaver W 1949 The Mathematical Theory of Communications (Champaign, IL: University of Illinois Press)
3. Borst A and Theunissen F 1999 Information theory and neural coding Nat. Neurosci. 2 947-57
4. Meister M and Berry M 1999 The neural code of the retina Neuron 22 435-50
5. Rieke F, Warland D, de Ruyter van Steveninck R and Bialek W 1997 Spikes: Exploring the Neural Code (Cambridge, MA: MIT Press)
6. Linsker R 1988 Self organisation in a perceptual network Comput. Mag. 21 105-17
7. Bell A and Sejnowski T 1997 The 'independent components' of natural scenes are edge filters Vision Res. 37 3327-38
8. Toyoizumi T, Aihara K and Gerstner W 2005 Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission Proc. Natl. Acad. Sci. USA 102 5239-44
9. Geisler W 1989 Sequential ideal-observer analysis of visual discriminations Psychological Rev. 96 267-314
10. Golden J and Shadlen M 2001 Neural computations that underlie decisions about sensory stimuli Trends Cogn. Sci. 5 10-6
11. Theunissen F and Miller J 1991 Representation of sensory information in the cricket cereal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons J. Neurophys. 66 1690-703
12. Brenner N, Bialek W and de Ruyter van Steveninck 2000 Adaptive rescaling maximizes information transmission Neuron 26 695-702
13. Huys Q, Zemel R, Natarajan R P and Dayan P 2007 Fast population coding Neural Comput. 19 460-97
14. Rao R and Ballard D 1997 Dynamic model of visual recognition predicts neural response properties in the visual cortex Neural Comput. 9 721-63
15. Beck J and Pouget A 2007 Exact inferences in a neural implementation of a hidden Markov model Neural Comput. 19 1344-61
16. Rao R 2004 Bayesian computation in recurrent neural circuits Neural Comput. 16 1-38
17. Rao R 2005 Hierarchical Bayesian inference in networks of spiking neurons Advances in Neural Information Processing Systems 17 ed L K Saul, Y Weiss and L Bottou (Cambridge, MA: MIT Press) pp 1113-20
18. Mainen Z and Sejnowski T 1995 Reliability of spike timing in neocortical neurons Science 268 1503-6
19. Dayan P and Abbott L 2001 Theoretical Neuroscience (Cambridge, MA: MIT Press)
20. Gerstner W and Kistler W 2002 Spiking Neuron Models: Single Neurons, Populations and Plasticity (Cambridge: Cambridge University Press)
21. Gutkin B and Ermentrout G 1998 Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics Neural Comput. 10 1047-65
22. Carandini M, Demb J, Mante V, Tolhurst D, Dan Y, Olshausen B, Gallant J and Rust N 2005 Do we know what the early visual system does? J. Neurosci. 25 10577-97
23. Chichilnisky E 2001 A simple white noise analysis of neuronal light responses Network 12 199-213
24. Denève S 2005 Bayesian inference in spiking neurons Advances in Neural Information Processing Systems 17 ed L K Saul, Y Weiss and L Bottou (Cambridge, MA: MIT Press) pp 353-60
25. Rabiner L 1989 A tutorial on hidden Markov models and selected applications in speech recognition Proc. IEEE 77 257-86
26. Denève S 2008 Bayesian spiking neurons I: inference Neural Comput. 20 91-117
27. Mongillo G and Denève S 2008 On-line learning with hidden Markov models Neural Comput. at press doi:10.1162/neco.2008.10-06-351
28. Troy J and Robson J 1992 Steady discharges of x and y retinal ganglion cells of cat under photopic illuminance Visual Neurosci. 9 535-53
29. Golomb D, Hertz J, Panzeri S, Treves A and Richmond B 1997 How well can we estimate the information carried in neuronal responses from limited samples? Neural Comput. 9 649-65
30. Paninski L 2003 Estimation of entropy and mutual information Neural Comput. 15 1191-254
31. Gautrais J and Thorpe S 1998 Rate coding versus temporal order coding: a theoretical approach BioSystems 48 57-65
32. McAdams C and Maunsell J 1999 Effects of attention on orientation-tuning functions of single neurons in macaque cortical area v4 J. Neurosci. 19 431-41
33. Treue S 2004 Perceptual enhancement of contrast by attention Trends Cogn. Sci. 8 435-7
34. Feng J and Brown D 2004 Decoding input signals in time domain - a model approach J. Comput. Neurosci. 16 237-49
35. Paninski L, Shoham S, Fellows M, Hatsopoulos N and Donoghue J 2004 Superlinear population encoding of dynamic hand trajectory in primary motor cortex J. Neurosci. 24 8551-61
36. Oram M, Wiener M, Lestienne R and Richmond B 1999 Stochastic nature of precisely timed spike patterns in visual system neuronal responses J. Neurophysiol. 81 3021-33
37. Richmond B and Optican L 1990 Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmissions Neurophysiol. 64 370-80
38. Stein R 1967 The information capacity of nerve cells using a frequency code Biophys. J. 7 797-826
39. Eden U T, Frank L M, Barbieri R, Solo V and Brown E N 2004 Dynamic analysis of neural encoding by point process adaptive filtering Neural Comput. 16 971-98
40. Pillow J, Paninski L, Uzzell V, Simoncelli E and Chichilnisky E 2005 Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model J. Neurosci. 25 11003-13
41. Fairhall A, Lewen G, Bialek W and de Ruyter van Steveninck R 2001 Efficiency and ambiguity in an adaptive neural code Nature 412 787-92
42. Hosoya T, Baccus S and Meister M 2005 Dynamic predictive coding by the retina Nature 436 71-7
43. Smirnakis S, Berry M, Warland D, Bialek W and Meister M 1997 Adaptation of retinal processing to image contrast and spatial scale Nature 386 69-73
44. Nakahara H, Wu S and Amari S-I 2001 Attention modulation of neural tuning through peak and base rate Neural Comput. 13 2031-47
45. Cameron E, Tai J and Carrasco M 2002 Covert attention affects the psychometric function of contrast sensitivity Vision Res. 42 949-67
46. Sclar G and Freeman R 1982 Orientation selectivity in the cat's striate cortex is invariant with stimulus contrast Exp. Brain Res. 46 457-61
47. Pouget A, Denève S, Ducom J-C and Latham P 1999 Narrow versus wide tuning curves: what's best for a population code? Neural Comput. 11 85-90
48. Rezaeian M 2006 Hidden Markov process: a new representation, entropy rate and estimation entropy Preprint cs.IT/0606114
49. Yu A J 2007 Optimal change-detection and spiking neurons Advances in Neural Information Processing Systems 19 ed L K Saul, Y Weiss and L Bottou (Cambridge, MA: MIT Press) pp 1545-52