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Bayes in the Brain
TJ Anastasio, PE Patton, K Belkacem-Boussaid. 2000. Using Bayes’ Rule to Model Multisensory Enhancement in the Superior Colliculus, Neural Computation, 12:1165–1187.
H Colonius, A Diederich. 2004. Why aren’t all deep superior colliculus neurons multisensory? A Bayes’ ratio analysis, Cognitive, Affective, & Behavioral Neuroscience, 4(3),344–353.
DC Knill, A Pouget A. 2004. The Bayesian brain: the role of uncertainty in neural coding and computation, Trends Neurosci. 2004 Dec;27(12):712–9.
P Patton, K Belkacem-Boussaid, TJ Anastasio Multimodality in the superior colliculus: an information theoretic analysis, Cognitive Brain Research, 14(1):10–19.
Perception
J Feldman. 2004. Bayes and the Simplicity Principle in Perception Rutgers Center for Cognitive Science Technical Report #80.
WS Geisler, D Kersten D. 2002. Illusions, perception and Bayes. Nat Neurosci., 5(6):598–604.
D Kersten, P Mamassian, A Yuille. 2004 Object Perception as Bayesian Inference. Annu Rev Psychol, 55:271–304.
D Kersten, A Yuille. 2003 Bayesian Models of Object Perception. Current Opinion in Neurobiology, 13:1–9.
Perception as Bayesian Inference, Edited by David C. Knill (Univ of PA), Whitman Richards (MIT), 1996. Hardback (ISBN-10: 052146109X | ISBN-13: 9780521461092)
M Sadakata, P Desain, H Honing. The relation between rhythm perception and production: towards a Bayesian model. Transaction of Technical Committee of Psychological and Physiological Acoustics, 32 (10), H-2002–92. (*note that this may be an older version)
PA van der Helm. 2000. Simplicity versus likelihood in visual perception: from surprisals to precisals, Psychological Bulletin, 126(5):770–800.
Z Yang, D Purves. 2003. A statistical explanation of visual space, Nature Neuroscience, 6(6):632–640.
Neural Dynamics
Shah A.S., Bressler S.L., Knuth K.H., Ding M., Mehta A.D., Ulbert I., Schroeder C.E. 2003. Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cerebral Cortex. (feature article)14: 476–483.
Data Analysis Techniques
Ruffini, Ray, Fuentemilla, Grau, 2005. Complex networks in brain electrical activity.
Larry Bretthorst. Bayesian Spectrum Analysis and Parameter Estimation. Lecture Notes in Statistics. 48. Springer
Knuth K.H., Vaughan H.G., Jr. 1999. Convergent Bayesian formulations of blind source separation and electromagnetic source estimation In: W. von der Linden, V. Dose, R. Fischer and R. Preuss (eds.), Maximum Entropy and Bayesian Methods, Munich 1998, Dordrecht. Kluwer, pp. 217–226.
Knuth K.H. 1998. Difficulties applying recent blind source separation techniques to EEG and MEG. In: G.J. Erickson, J.T. Rychert and C.R. Smith (eds.), Maximum Entropy and Bayesian Methods, Boise 1997, Kluwer, Dordrecht, pp. 209–222.
E Pereda, RQ Quiroga, J. Bhattacharya. 2005. Nonlinear Multivariate Analysis of Neurophysiological Signals, to appear in Progess in Neurobiology.
DM Schmidt, JS George, DM Ranken, CC Wood. 2000. Spatial-temporal Bayesian inference for MEG/EEG in Biomag2000, Proc. 12th Int. Conf. on Biomagnetism, J. Nenonen, R.J. Ilmoniemi, and T. Katila, eds. (Helsinki Univ. of Technology, Espoo, Finland, 2001), pp. 671–673.
Truccolo W.A., Knuth K.H., Shah A.S., Bressler S.L., Schroeder C.E., and Ding M. 2003. Estimation of single-trial multi-component ERPs: Differentially variable component analysis. Biol. Cybern. 89(6): 426–38.
