Error-probability noise benefits in threshold neural signal detection

Neural Networks

Volumn 22, Issue 5-6, 2009, Pages 697-706

  Patel, Ashok ^a   Kosko, Bart ^a  

^a University of Southern California   (United States)

Author keywords

Array detection; Error probability; Optimal noise intensity; Signal detection; Stochastic resonance; Threshold neurons

Indexed keywords

ARRAY DETECTION; ERROR PROBABILITY; OPTIMAL NOISE INTENSITY; STOCHASTIC RESONANCE; THRESHOLD NEURONS;

CIRCUIT RESONANCE; ERROR DETECTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAGNETIC RESONANCE; NEURONS; OPTIMIZATION; PROBABILITY; PROBABILITY DENSITY FUNCTION; SCALES (WEIGHING INSTRUMENTS);

SIGNAL DETECTION;

ANALYTICAL ERROR; ARTICLE; CONTROLLED STUDY; LEARNING ALGORITHM; NERVE CELL; NORMAL DISTRIBUTION; PRIORITY JOURNAL; PROBABILITY; SIGNAL DETECTION; SIGNAL NOISE RATIO; SYSTEMS THEORY;

ALGORITHMS; LEARNING; MODELS, NEUROLOGICAL; NEURONS; NORMAL DISTRIBUTION; PROBABILITY; SIGNAL DETECTION, PSYCHOLOGICAL; STOCHASTIC PROCESSES;

EID: 68149181688     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.06.044     Document Type: Article

References (62)

1. Amblard P.-O., Zozor S., McDonnell M.D., and Stocks N.G. Pooling networks for a discrimination task: Noise-enhanced detection Vol. 6602 (2007), SPIE 66020S
2. Auer P., Burgsteiner H., and Maass W. A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks 21 5 (2008) 786-795
3. Bazi Y., Bruzzone L., and Melgani F. Image thresholding based on the em algorithm and the generalized Gaussian distribution. Pattern Recognition 40 2 (2007) 619-634
4. Beiu V., Member S., Quintana J.M., and Avedillo M.J. Vlsi implementations of threshold logic - a comprehensive survey. IEEE Transactions on Neural Networks 14 (2003) 1217-1243
5. Bulsara A., Jacobs E., Zhou T., Moss F., and Kiss L. Stochastic resonance in a single neuron model: Theory and analog simulation. Journal of Theoretical Biology 152 (1991) 531-555
6. Casella G., and Berger R. Statistical inference (2001), Duxbury Resource Center
7. Caticha N., Palo Tejada J.E., Lancet D., and Domany E. Computational capacity of an odorant discriminator: The linear separability of curves. Neural Computation 14 9 (2002) 2201-2220
8. Chapeau-Blondeau F., and Rousseau D. Noise-enhanced performance for an optimal Bayesian estimator. Signal Processing, IEEE Transactions on 52 5 (2004) 1327-1334
9. Deco G., and Schürmann B. Stochastic resonance in the mutual information between input and output spike trains of noisy central neurons. Physica D 117 1-4 (1998) 276-282
10. Faisal A.A., Selen L.P., and Wolpert D.M. Noise in the nervous system. Nature Reviews Neuroscience 9 4 (2008) 292-303
11. Folland G.B. Real analysis: Modern techniques and their applications. second ed. (1999), Wiley-Interscience
12. Freund Y., and Schapire R.E. Large margin classification using the perceptron algorithm Vol. 37 (1999) 277-296
13. Gammaitoni L. Stochastic resonance and the dithering effect in threshold physical systems. Physical Review E 52 5 (1995) 4691-4698
14. Gazor S., and Zhang W. Speech probability distribution. Signal Processing Letters, IEEE 10 7 (2003) 204-207
15. Grigoriu M. Applied non-Gaussian processes (1995), Prentice Hall
16. Hänggi P. Stochastic resonance in biology. ChemPhysChem 3 (2002) 285-290
17. Hoch T., Wenning G., and Obermayer K. Optimal noise-aided signal transmission through populations of neurons. Physical Review E 68 (2003) 011911-1-11
18. Jones D. The theory of generalized functions. second ed. (1982), Cambridge University Press
19. Kay S. Can detectability be improved by adding noise?. Signal Processing Letters, IEEE 7 1 (2000) 8-10
20. Kay S., Michels J., Chen H., and Varshney P. Reducing probability of decision error using stochastic resonance. Signal Processing Letters, IEEE 13 11 (2006) 695-698
21. Kosko B. Noise (2006), Viking/Penguin
22. Kosko B., and Mitaim S. Stochastic resonance in noisy threshold neurons. Neural Networks 16 5-6 (2003) 755-761
23. Kosko B., and Mitaim S. Robust stochastic resonance for simple threshold neurons. Physical Review E 70 (2004) 031911-1-10
24. Krupiński R., and Purczyński J. Approximated fast estimator for the shape parameter of generalized Gaussian distribution. Signal Processing 86 2 (2006) 205-211
25. Läer L., Kloppstech M., Schöfl C., Sejnowski T.J., Brabant G., and Prank K. Noise enhanced hormonal signal transduction through intracellular calcium oscillations. Biophysical Chemistry 91 (2001) 157-166
26. Levy W.B., and Baxter R.A. Energy-efficient neuronal computation via quantal synaptic failures. Journal of Neuroscience 22 (2002) 4746-4755
27. Li H., Hou Z., and Xin H. Internal noise enhanced detection of hormonal signal through intracellular calcium oscillations. Chemical Physics Letters 402 4-6 (2005) 444-449
28. Manwani A., and Koch C. Detecting and estimating signals in noisy cable structures, i: Neuronal noise sources. Neural Computation 11 9 (1999) 1797-1829
29. Markram H., and Tsodyks M. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382 (1996) 807-810
30. McCulloch W.S., and Pitts W. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (1943) 115-133 Reprinted in Neurocomputing: Foundations of Research, ed. by J. A. Anderson and E Rosenfeld. MIT Press 1988
31. McDonnell M., Stocks N., Pearce C., and Abbott D. Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance. Physics Letters A 352 (2006) 183-189
32. McDonnell M.D., Stocks N.G., Pearce C.E.M., and Abbott D. Stochastic resonance: From suprathreshold stochastic resonance to stochastic signal quantization (2008), Cambridge University Press
33. Min M.Y., and Appenteng K. Multimodal distribution of amplitudes of miniature and spontaneous epsps recorded in rat trigeminal motoneurones. Journal Physiology 494 (1996) 171-182
34. Minsky M., and Papert S. Perceptrons: An introduction to computational geometry. expanded ed. (1988), MIT Press, Cambridge, MA
35. Mitaim S., and Kosko B. Adaptive stochastic resonance. Proceedings of the IEEE 86 11 (1998) 2152-2183
36. Mitaim S., and Kosko B. Adaptive stochastic resonance in noisy neurons based on mutual information. IEEE Transactions on Neural Networks 15 6 (2004) 1526-1540
37. Moss F., Ward L.M., and Sannita W.G. Stochastic resonance and sensory information processing: A tutorial and review of application. Clin Neurophysiol 115 2 (2004) 267-281
38. Nadarajah S. A generalized normal distribution. Journal of Applied Statistics 32 7 (2005) 685-694
39. Nikias C.L., and Shao M. Signal processing with alpha-stable distributions and applications (1995), Wiley-Interscience, New York, NY, USA
40. Pantazelou E., Dames C., Moss F., Douglass J., and Wilkens L. Temperature dependence and the role of internal noise in signal transduction efficiency of crayfish mechanoreceptors. International Journal of Bifurcation and Chaos 5 (1995) 101-108
41. Patel A., and Kosko B. Stochastic resonance in noisy spiking retinal and sensory neuron models. Neural Networks 18 5-6 (2005) 467-478
42. Patel A., and Kosko B. Stochastic resonance in continuous and spiking neuron models with levy noise. Neural Networks, IEEE Transactions on 19 12 (2008) 1993-2008
43. Patel, A., & Kosko, B. (2009a). Neural signal-detection noise benefits based on error probability. In Proceedings of the international joint conference on neural networks (pp. 2423-2430)
44. Patel A., and Kosko B. Optimal noise benefits in neyman-pearson and inequality-constrained statistical signal detection. IEEE Transactions on Signal Processing 57 5 (2009) 1655-1669
45. Patel, A., & Kosko, B. (2009c). Quantizer noise benefits in nonlinear signal detection with alpha-stable channel noise. In IEEE international conference on acoustic, speech and signal processing (pp. 3269-3272)
46. Proakis J., and Salehi M. Digital communications. fifth ed. (2008), McGraw Hill
47. Rao C.V., Wolf D.M., and Arkin A.P. Control, exploitation and tolerance of intracellular noise. Nature 420 6912 (2002) 231-237
48. Rousseau D., Anand G.V., and Chapeau-Blondeau F. Noise-enhanced nonlinear detector to improve signal detection in non-Gaussian noise. Signal Processing 86 11 (2006) 3456-3465
49. Rousseau D., and Chapeau-Blondeau F. Constructive role of noise in signal detection from parallel array of quantizers. Signal Processing 85 3 (2005) 571-580
50. Rousseau D., and Chapeau-Blondeau F. Stochastic resonance and improvement by noise in optimal detection strategies. Digital Signal Processing 15 1 (2005) 19-32
51. Saha A.A., and Anand G.V. Design of detectors based on stochastic resonance. Signal Processing 83 6 (2003) 1193-1212
52. Sasaki H., Sakane S., Ishida T., Todorokihara M., Kitamura T., and Aoki R. Suprathreshold stochastic resonance in visual signal detection. Behavioural Brain Research 193 1 (2008) 152-155
53. Schneidman E., Freedman B., and Segev I. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Computation 10 7 (1998) 1679-1703
54. Stacey W.C., and Durand D.M. Stochastic resonance improves signal detection in hippocampal ca1 neurons. Journal Neurophysiology 83 (2000) 1394-1402
55. Stocks N., Appligham D., and Morse R. The application of suprathreshold stochastic resonance to cochlear implant coding. Fluctuation and Noise Letters 2 (2002) L169-L181
56. Stocks N.G. Information transmission in parallel threshold arrays: Suprathreshold stochastic resonance. Physical Review E 63 4 (2001) 041114
57. Stocks N.G., and Mannella R. Generic noise-enhanced coding in neuronal arrays. Physical Review E 64 3 (2001) 030902-1-4
58. Wang W., and Wang Z.D. Internal-noise-enhanced signal transduction in neuronal systems. Physical Review E 55 6 (1997) 7379-7384
59. White J.A., Rubinstein J.T., and Kay A.R. Channel noise in neurons. Trends in Neuroscience 23 3 (2000) 131-137
60. Wiesenfeld K., and Moss F. Stochastic resonance and the benefits of noise: From ice ages to crayfish and squid. Nature (London) 373 (1995) 33
61. Wimmer K., Hildebrandt K., Hennig R., and Obermayer K. Adaptation and selective information transmission in the cricket auditory neuron AN2. PLoS Computational Biology 4 9 (2008) e1000182+
62. Zemanian A.H. Distribution theory and transform analysis: An introduction to generalized functions, with applications (1987), Dover Publications, Inc, New York, NY, USA