State-space algorithms for estimating spike rate functions

Computational Intelligence and Neuroscience

Volumn 2010, Issue , 2010, Pages

  Smith, Anne C ^a   Scalon, Joao D ^b   Wirth, Sylvia ^c   Yanike, Marianna ^d   Suzuki, Wendy A ^d   Brown, Emery N ^e,f  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b FEDERAL UNIVERSITY OF LAVRAS   (Brazil)

^c INSTITUT DES SCIENCES COGNITIVES   (France)

^d Columbia University ^*  (United States)

^e MASSACHUSETTS GENERAL HOSPITAL   (United States)

^f MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; COMPUTATIONALLY EFFICIENT; CONFIDENCE INTERVAL; GOODNESS OF FIT; MAXIMUM LIKELIHOOD ESTIMATE; RATE FUNCTIONS; REGRESSION SPLINES; SIMPLE CUBIC; SMOOTHING ALGORITHMS; SPIKE FIRING RATES; SPLINE APPROACH; STATE SPACE APPROACH; STATE-SPACE; STATE-SPACE MODELS;

MAXIMUM LIKELIHOOD ESTIMATION; SPLINES;

ESTIMATION;

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; CONFIDENCE INTERVAL; ELECTROPHYSIOLOGY; EYE MOVEMENT; HIPPOCAMPUS; LEARNING; MACACA; NERVE CELL; PHYSIOLOGY; REGRESSION ANALYSIS; STATISTICAL MODEL;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; ASSOCIATION LEARNING; BAYES THEOREM; COMPUTER SIMULATION; CONFIDENCE INTERVALS; ELECTROPHYSIOLOGY; EYE MOVEMENTS; HIPPOCAMPUS; MACACA; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NEURONS; REGRESSION ANALYSIS;

EID: 77449118575     PISSN: 16875265     EISSN: 16875273     Source Type: Journal    
DOI: 10.1155/2010/426539     Document Type: Article

References (37)

1. Koelling M. E., Nykamp D. Q., Computing linear approximations to nonlinear neuronal response Network: Computation in Neural Systems 2008 19 4 286 313
2. Ahrens M. B., Paninski L., Sahani M., Inferring input nonlinearities in neural encoding models Network: Computation in Neural Systems 2008 19 1 35 67
3. Czanner G., Eden U. T., Wirth S., Yanike M., Suzuki W. A., Brown E. N., Analysis of between-trial and within-trial neural spiking dynamics Journal of Neurophysiology 2008 99 5 2672 2693
4. Huys Q. J. M., Ahrens M. B., Paninski L., Efficient estimation of detailed single-neuron models Journal of Neurophysiology 2006 96 2 872 890
5. Mullowney P., Iyengar S., Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data Journal of Computational Neuroscience 2008 24 2 179 194
6. Nalatore H., Ding M., Rangarajan G., Denoising neural data with state-space smoothing: method and application Journal of Neuroscience Methods 2009 179 1 131 141
7. Paninski L., The most likely voltage path and large deviations approximations for integrate-and-fire neurons Journal of Computational Neuroscience 2006 21 1 71 87
8. Paninski L., Fellows M. R., Hatsopoulos N. G., Donoghue J. P., Spatiotemporal tuning of motor cortical neurons for hand position and velocity Journal of Neurophysiology 2004 91 1 515 532
9. Truccolo W., Eden U. T., Fellows M. R., Donoghue J. P., Brown E. N., A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects Journal of Neurophysiology 2005 93 2 1074 1089
10. Behseta S., Kass R. E., Testing equality of two functions using BARS Statistics in Medicine 2005 24 22 3523 3534
11. Behseta S., Kass R. E., Moorman D. E., Olson C. R., Testing equality of several functions: analysis of single-unit firing-rate curves across multiple experimental conditions Statistics in Medicine 2007 26 21 3958 3975
12. Kaufman C. G., Ventura V., Kass R. E., Spline-based non-parametric regression for periodic functions and its applications to directional tuning of neurons Statistics in Medicine 2005 24 14 2255 2265
13. Wallstrom G., Liebner J., Kass R. E., An implementation of Bayesian adaptive regression splines (BARS) in C with S and R wrappers Journal of Statistical Software 2008 26 1 1 21
14. Kulkarni J. E., Paninski L., State-space decoding of goal-directed movements IEEE Signal Processing Magazine 2008 25 1 78 86
15. Smith A. C., Brown E. N., Estimating a state-space model from point process observations Neural Computation 2003 15 965 991
16. Durbin J., Koopman S. J., Time Series Analysis by State Space Methods 2001 Oxford, UK Oxford University Press
17. Kashiwagi N., Yanagimoto T., Smoothing serial count data through a state-space model Biometrics 1992 48 4 1187 1194
18. Kitagawa G., Gersch W., Smoothness Priors Analysis of Time Series 1996 New York, NY, USA Springer
19. Brown E. N., Nguyen D. P., Frank L. M., Wilson M. A., Solo V., An analysis of neural receptive field plasticity by point process adaptive filtering Proceedings of the National Academy of Sciences of the United States of America 2001 98 21 12261 12266
20. Wirth S., Yanike M., Frank L. M., Smith A. C., Brown E. N., Suzuki W. A., Single neurons in the monkey hippocampus and learning of new associations Science 2003 300 5625 1578 1581
21. DiMatteo I., Genovese C. R., Kass R. E., Bayesian curve-fitting with free-knot splines Biometrika 2001 88 4 1055 1071
22. Olson C. R., Gettner S. N., Ventura V., Carta R., Kass R. E., Neuronal activity in macaque supplementary eye field during planning of saccades in response to pattern and spatial cues Journal of Neurophysiology 2000 84 3 1369 1384
23. Kitagawa G., Non-Gaussian state-space modeling of nonstationary times series Journal of the American Statistical Association 1987 82 1032 1041
24. Fahrmeir L., Tutz G., Multivariate Statistical Modelling Based on Generalized Linear Models 2001 2nd New York, NY, USA Springer
25. Roweis S., Ghahramani Z., A unifying review of linear Gaussian models Neural Computation 1999 11 2 305 345
26. Daley D. J., Vere-Jones D., An Introduction to the Theory of Point Processes 2003 2nd New York, NY, USA Springer
27. Kalbfleisch J. D., Prentice R. L., The Statistical Analysis of Failure Time Data 2002 2nd Hoboken, NJ, USA John Wiley Sons
28. Brown E. N., Chow C. C., Gutkin B., Hansel D., Meunier C., Dalibard J., Theory of point processes for neural systems Methods and Models in Neurophysics 2005 Paris, France Elsevier 691 726
29. Dempster A. P., Laird N. M., Rubin D. B., Maximum likelihood from incomplete data via EM algorithm The Journal of the Royal Statistical Society, Series B 1977 39 1 38
30. Hoel P. G., Port S. C., Stone C. J., Introduction to Probability Theory 1971 Boston, Mass, USA Houghton Mifflin
31. Smith A. C., Stefani M. R., Moghaddam B., Brown E. N., Analysis and design of behavioral experiments to characterize population learning Journal of Neurophysiology 2005 93 3 1776 1792
32. Johnson N. L., Kotz S., Continuous Univariate Distributions 1970 New York, NY, USA John Wiley Sons
33. Kass R. E., Ventura V., Cai C., Statistical smoothing of neuronal data Network: Computation in Neural Systems 2003 14 1 5 15
34. Denison D. G. T., Mallick B. K., Smith A. F. M., Automatic Bayesian curve fitting Journal of the Royal Statistical Society. Series B 1998 60 2 333 350
35. Box G. E. P., Jenkins G. M., Reinsel G. C., Time Series Analysis: Forecasting and Control 2008 4th Hoboken, NJ, USA John Wiley Sons
36. Smith A. C., Frank L. M., Wirth S., Dynamic analysis of learning in behavioral experiments Journal of Neuroscience 2004 24 2 447 461
37. Congdon P., Applied Bayesian Modelling 2003 Chichester, UK John Wiley Sons