Can we identify non-stationary dynamics of trial-to-trial variability?

PLoS ONE

Volumn 9, Issue 4, 2014, Pages

  Balaguer Ballester, Emili ^a,b   Tabas Diaz, Alejandro ^a   Budka, Marcin ^a  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

^b MEDICAL FACULTY MANNHEIM HEIDELBERG UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; DATA ANALYSIS; DECISION MAKING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MATHEMATICAL PHENOMENA; MATHEMATICAL VARIABLE; NONSTATIONARY DYNAMICS; PROBABILITY; PROCESS DESIGN; PROCESS DEVELOPMENT; QUALITY CONTROL; TREND STUDY; TRIAL TO TRIAL VARIABILITY; ALGORITHM; ANIMAL; CENTRAL NERVOUS SYSTEM; HUMAN; METHODOLOGY; NEUROLOGIC EXAMINATION; NONLINEAR SYSTEM; PHYSIOLOGY; RAT; REPRODUCIBILITY;

ALGORITHMS; ANIMALS; CENTRAL NERVOUS SYSTEM; DIAGNOSTIC TECHNIQUES, NEUROLOGICAL; HUMANS; NONLINEAR DYNAMICS; RATS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN;

EID: 84899729782     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0095648     Document Type: Article

References (83)

1. Mann M (2004) On smoothing potentially non-stationary climate time series. Geophys Res Lett 31: L07214.
2. Haraa S, Kawaharaa Y, Washioa T, von Bnau P, Tokunagac T, et al. (2012) Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Networks 33: 7-20.
3. Csorgo H, Horvarth L (2009) Nonparametric methods for change point problems, Kluwer Academic Pub., volume 7. pp. 403-425.
4. von Bunau P, Meinecke F, Kiraly F, Robert-Muller K (2009) Finding stationary subspaces in multivariate time series. Phys Rev Lett 103: 214101.
5. Masquelier T (2013) Neural variability, or lack thereof. Frontiers in Comput Neurosci 7: 1-7.
6. Quiroga Lombard C, Hass J, Durstewitz D (2013) A method for stationarity-segmentation of spike train data with application to the pearson cross-correlation. J Neurophysiol 110: 562-572.
7. Churchland M, Abbot L (2012) Two layers of neural variability. Nat Neurosci 15: 1472-1474.
8. Zohary E, Shadlen M, Newsome W (1994) Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370: 140-143. (Pubitemid 24229125)
9. Whitsel B, Schreiner R, Essick G (1977) Analysis of variability in somatosensory cortical neuron discharge. J Neurophysol 40: 589-607.
10. Werner G, Mountcastle V (1963) Variability of central neural activity in a sensory system, and its implications for central reection of sensory events. J Neurophysol 29: 958-977.
11. Durstewitz D, Vittoz N, Floresco S, Seamans J (2010) Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66: 438-448.
12. Churchland M, Afshar A, Shenoy K (2006) A central source of movement variability. Neuron 52: 1085-1096. (Pubitemid 44909405)
13. Beck J, Ma WJ, Pitkow X, Latham PE, Pouget A (2012) Not noisy, just wrong: The role of suboptimal inference in behavioral variability. Neuron 74: 33-39.
14. Toups J, Fellous JM, Thomas P, Sejnowski T, Tiesinga P (2012) Multiple spike time patterns occur at bifurcation points of membrane potential dynamics. PLoS Comput Biol 8: e1002615.
15. Deco V Gand Jirsa (2012) Ongoing cortical activity at rest: criticality, multistability and ghost attractors. J Neurosci 32: 3366-3375.
16. Braun J, Mattia M (2010) Attractors and noise: twin drivers of decisions and multistability. Neuroimage 52: 740-751.
17. Bernal-Casas D, Balaguer-Ballester E, Gerchen M, Iglesias S, Walter H, et al. (2013) Multi-site reproducibility of prefrontal-hippocampal connectivity estimates by stochastic dynamic causal models. Neuroimage 82: 555-563.
18. Churchland M, Yu B, Cunningham J, Sugrue L, Cohen M, et al. (2010) Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci 13: 369-378.
19. Hyman J, Ma L, Balaguer-Ballester E, Durstewitz D, Seamans J (2012) Contextual encoding by ensembles of medial prefrontal cortex neurons. PNAS 109: 5086-5091.
20. Scaglione A, Moxon K, Aguilar J, Foffani G (2011) Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus. PNAS 108: 14956-61.
21. Deneve S, Latham P, Pouget A (2001) Efficient computation and cue integration with noisy population codes. Nat Neurosci 4: 826-831. (Pubitemid 32702515)
22. Stiefel K, Englitz B, Sejnowski T (2013) Origin of intrinsic irregular firing in cortical interneurons. PNAS 110: 7886-91.
23. Faisal A, Selen L, Wolpert D (2008) Noise in the nervous system. Nat Rev Neurosci 66: 292-303. (Pubitemid 351430828)
24. Moreno-Bote R, Knill D, Pouget A (2011) Bayesian sampling in visual perception. PNAS 108: 12491-12496.
25. Sussillo D, Abbott L (2009) Generating coherent patterns of activity from chaotic neural networks. Neuron 63: 544-557.
26. Litwin-Kumar A, Doiron B (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci 15: 1498-1505.
27. Renart A, de la Rocha J, Bartho P, Hollender L, Parga N, et al. (2010) The asynchronous state in cortical circuits. Science 327: 587-590.
28. Blythe DAJ, von Bunau P, Meinecke FC, Robert-Muller K (2012) Feature extraction for changepoint detection using stationary subspace analysis. IEEE Trans Neural Networks 23: 631-643.
29. Kuncheva L (2013) Change detection in streaming multivariate data using likelihood detectors. IEEE Transactions on Knowledge and Data Engineering 25: 1175-1180.
30. Zliobaite I, Bifet A, Pfahringer B, Holmes G (2013) Active learning with drifting streaming data. IEEE Transactions on Neural Networks and Learning Systems In press.
31. Volpi M, Tuia D, Camps-Valls G, Kanevski M (2012) Unsupervised change detection with kernels. IEEE Geosciences and Remote Sensing Letters 9: 1026-1030.
32. Nason G (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J R Statist Soc B 75: 879904.
33. Chen J, Hu N (2014) A frequency domain test for detecting nonstationary time series. Computational Statistics and Data Analysis In press.
34. Priestley M, Subba T (1969) A test for non-stationarity of time-series. J R Statist Soc B 31: 140149.
35. Bouchachia A (2011) Incremental learning with multi-level adaptation. Neurocomputing 74: 1785-1799.
36. Sayed-Mouchaweh M, Lughofer E, editors(2012) Learning in Non-Stationary Environments. Springer.
37. Honeine P (2012) Online kernel principal component analysis: A reduced-order model. Journal of Machine Learning Research 34: 1814-1826.
38. Brazdil P, Gama J, Soares C (2009) Meta-Learning: Applications to Data Mining. Springer.
39. Robinson J, Hartemink A (2010) Learning non-stationary dynamic bayesian networks. Journal of Machine Learning Research 11: 3647-3680.
40. Du J, Cui M (2010) Solving the forced duffing equation with integral boundary conditions in the reproducing kernel space. International Journal of Computer Mathematics 87: 2088-2100.
41. Sabarathinama S, Thamilmaran K, Borkowski L, Perlikowski P, Brzeski P, et al (2013) 10: 3098-3017.
42. Wiggins S (2013) Introduction to applied nonlinear dynamical systems and chaos. Springer.
43. Holmes P, Whitley D (1983) On the attracting set for duffing's equation. Physica D 7: 111-123.
44. Jiang ZP (2002) Advanced feedback control of the chaotic duffinng equation. IEEE Trans Circuits Syst 49: 244-249.
45. Feng Z, Chen G, Hsu S (2006) A qualitative study of the damped duffing equation and applications. American Institute of Mathematical Sciences 6: 1097-1112. (Pubitemid 44367314)
46. Ha J, Nakagiri S (2004) Identification problems for the damped klein-gordon equation. Math Anal Appl 289: 77-89. (Pubitemid 37551210)
47. Freyer F, Roberts J, Ritter P, Breakspear M (2012) A canonical model of multistability and scaleinvariance in biological systems. PLoS Comput Biol 8: e1002634.
48. Balaguer-Ballester E, Lapish C, Seamans J, Durstewitz D (2011) Attracting dynamics of frontal cortex ensembles during memory guided decision making. PLoS Computational Biology 7: e1002057
49. Lapish C, Durstewitz D, Chandler L, Seamans J (2008) Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex. Proc Natl Acad Sci USA 105: 12010-12015 (*first two authors contributed equally).
50. Balaguer-Ballester E, Clark N, Coath M, Krumbholz K, Denham S (2009) Understanding pitch perception as a hierarchical process with top-down modulation. PLoS Comput Biol 5: e1000301.
51. Stein R, Gossen E, Jones KE (2005) Neuronal variability: noise or part of the signal? Nat Rev Neurosci 6: 389-397. (Pubitemid 40593484)
52. Gama J, Sebastio R, Rodrigues P (2013) On evaluating stream learning algorithms. Machine Learning 90: 317-346.
53. Sauer T, Yorke J, Casdagli M (1992) Embedology. J Stat Phys 65: 579-616.
54. Provenzale A, Smith L, R V, Murante G (1992) Distinguishing between low-dimensional dynamics and randomness in measured time series. Physica D 5: 28-31.
55. Bishop C (2007) Pattern recognition and machine learning. Springer.
56. Budka M, Gabrys B (2011) Electrostatic field framework for supervised and semi-supervised learning from incomplete data. Natural Computing 10: 921-945.
57. Kantz H, Schreiber T (2004) Nonlinear time series analysis. Cambridge University Press.
58. Balaguer-Ballester E, Soria E, Palomares A, Martn-Guerrero J (2008) Predicting service request in support centres based on nonlinear dynamics, arma modelling and neural networks. Expert Sys with App 34: 665-672. (Pubitemid 46778983)
59. Scholkopf B, Smola A (2002) Learning with kernels. MIT Press.
60. Saadi K, Talbot N, Cawley G (2007) Optimally regularised kernel fisher discriminant classification. Neural Networks 20: 832-841. (Pubitemid 47361672)
61. Schapire R, Freund Y, Bartlett P, Sun Lee W (1998) Boosting the margin: A new explanation for the effectiveness of voting methods. The annals of statistics 26: 1651-1686. (Pubitemid 128376902)
62. Gomez-Sanchis J, Martin-Guerrero J, Soria-Olivas E, Vila-Frances J, Carrasco J, et al. (2006) Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration. Atmospheric Environment 40: 6173-6180.
63. Balaguer-Ballester E, Camps-Valls G, Carrasco-Rodriguez JL, E S, del Valle-Tascon S (2002) Effective one-day ahead prediction of hourly surface ozone concentrations in eastern spain using linear models and neural networks. Ecological Modelling 156: 27-41.
64. Deco G, Jirsa V, McIntosh A (2011) Emerging concepts for the dynamical organization of restingstate activity in the brain. Nat Rev Neurosci 12: 43-56.
65. Staude B, Grun S, Rotter S (2010) Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference. Front Comput Neurosci 16: doi:10.3389/fncom.2010.00016
66. Balaguer-Ballester E, Coath M, Denham S (2007) A model of perceptual segregation based on clustering the time series of the simulated auditory nerve firing probability. Biol Cybern 97: 479-491.
67. Durstewitz D, Balaguer-Ballester E (2010) Statistical approaches for reconstructing neuro-cognitive dynamics from high-dimensional neural recordings. Neuroforum 1: 89-98.
68. Niessing J, Friedrich R (2010) Olfactory pattern classification by discrete neuronal network states. Nature 465: 47-54.
69. Wills T, Lever C, Cacucci F, Burgess N, O'Keefe J (2005) Attractor dynamics in the hippocampal representation of the local environment. Science 308: 873-876. (Pubitemid 40629271)
70. Mazor O, Laurent G (2005) Transient dynamics versus fixed points in odor representations by locust antennal lobe projection neurons. Neuron 48: 661-673. (Pubitemid 41654686)
71. Bathellier B, Buhl D, Accolla R, Carleton A (2008) Dynamic ensemble odor coding in the mamalian olfactory bulb: Sensory information at different time scales. Neuron 57: 586-598. (Pubitemid 351274002)
72. Vapnik V (1998) Statistical learning theory. Wiley-Interscience.
73. Milad L, Ping Z, Srikanta S, Taro T (2013) Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using gaussian mixture kalman filtering. Frontiers in Comput Neurosci 7: 00109.
74. Paninski L, Vidne M, DePasquale B, Ferreira DG (2012) Inferring synaptic inputs given a noisy voltage trace via sequential monte carlo methods. J Comput Neurosci 33: 1-19.
75. Kobayashi R, Shinomoto S, Lansky P (2012) Estimation of time-dependent input from neuronal membrane potential. Neural Comput 23: 3070-3093.
76. Ledberg A, Montagnini A, Coppola R, Bressler S (2012) Reduced variability of ongoing and evoked cortical activity leads to improved behavioral performance. PLoS ONE 7: e43166.
77. Rabinovich M, Huerta R, Laurent G (2008) Transient dynamics for neural processing. Science 321: 48-50.
78. Rabinovich M, Varona P (2011) Robust transient dynamics and brain functions. Front Comput Neurosci 6: doi:10.3389/fncom.2011.00024
79. Ben-Hur A, Ong C, Sonnenburg S, Scholkopf B, Ratsch G (2008) Support vector machines and kernels for computational biology. PLoS Comput Biol 4: e1000173.
80. Smola A, vri Z, Williamson R (2001) Regularization with dot-product kernels, MA: MIT Press, volume 7. pp. 308-314.
81. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer.
82. Duong T, Hazelton M (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scand J Statist 32: 485-506. (Pubitemid 41251539)
83. Omi T, Shinomoto S (2011) Optimizing time histograms for non-poissonian spike trains. Neural Computation 23: 3125-3144.