ATTNEAVE F. (1954). Some informational aspects of visual perception. Psychological review. 61 [PubMed]

Aida T. (1999). Field theoretical analysis of on-line learning of probability distributions Phys Rev Lett. 83

Atick J. (1992). Could information theory provide an ecological theory of sensory processing? Princeton Lectures On Biophysics.

BARLOW HB. (1961). Possible principles underlying the transformations of sensory messages Sensory Communication.

Balasubramanian V. (1997). Statistical inference, Occam's razor, and statistical mechanics on the space of probability distributions Neural Comput. 9

Barlow H. (1959). Sensory mechanism, the reduction of redundancy, and intelligence National Physical Laboratory Symposium N. 10 The Mechanization of Thought Processes.

Bernardo J. (2003). Bayesian statistics UNESCO Encyclopedia of Life Support Systems (EOLSS).

Bialek W, Atwal G. (2004). Ambiguous model learning made unambiguous with 1-f, priors Advances in neural information processing systems. 16

Bialek W, Tishby N, Nemenman I. (2001). Predictability, complexity, and learning. Neural Comput. 13

Brenner N, Bialek W, de Ruyter van Steveninck R. (2000). Adaptive rescaling maximizes information transmission. Neuron. 26 [PubMed]

Clarke BS, Barron AR. (1990). Information theoretic asymptotics of Bayes methods IEEE Transactions On Information Theory. 36

Cover TM, Thomas JA. (1991). Elements of Information Theory.

Csiszar I. (1975). I-divergence geometry of probability distributions and minimization problems Annals Of Probability. 3

Dawid A. (1984). Present position and potential developments: Some personal views. Statistical theory: The prequential approach J Roy Stat Soc A. 147

Gallistel CR, Mark TA, King AP, Latham PE. (2001). The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect. Journal of experimental psychology. Animal behavior processes. 27 [PubMed]

Hall P, Hannan E. (1988). On stochastic complexity and nonparametric density estimation Biometrika. 75

Holy T. (1997). Analysis of data from continuous probability distributions Phys Rev Lett. 79

James W, Stein C. (1961). Estimation with quadratic loss Proc Fourth Berkeley Symposium Mathematical Statistics And Probability. 1

Janes E. (1979). Inference, method, and decision: Towards a Bayesian philosophy of science J Amer Stat Assoc. 74

Jeffreys H. (1936). Further significance tests Proc Camb Phil Soc. 32

Lemm J. (2002). Bayesian field theory.

Ma S. (1985). Statistical mechanics.

Mackay D. (1992). Bayesian Interpolation Neural Comput. 4

Nemenman I. (2000). Information theory and learning: A physical approach Unpublished doctoral dissertation.

Neter J, Kutner M, Nachtsheim C, Wasserman W. (1996). Applied linear regression models (3rd ed).

Press S. (1989). Bayesian statistics: Principles, models, and applications.

Rao RP. (2004). Bayesian computation in recurrent neural circuits. Neural computation. 16 [PubMed]

Reichardt W. (1961). Autocorrelation: A principle for the evaluation of sensory information by the nervous system Sensory Communication.

Rissanen J. (1989). Stochastic Complexity Statistical Inquiry.

Rissanen J, Speed T, Yu B. (1992). Density estimation by stochastic complexity IEEE Trans Inform Theory. 38

Samko S, Kilbas A, Marichev O. (1987). Integraly i proizvodnye drobnogo poriadka i nekotorye ikh prilozheniia.

Schwartz G. (1978). Estimating the dimension of a model Ann Stat. 6

Seung HS. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron. 40 [PubMed]

Smale S, Cucker F. (2001). On the mathematical foundations of learning Bull Amer Math Soc. 39

Stone M. (1977). An asymptotic equivalence of choice of model by cross-validation and Akaikes criterion J Roy Stat Soc B. 39

Vapnik V. (1998). Statistical Learning Theory.

Weiss TF. (1995). Cellular Biophysics Electrical Properties. 2

Wolpert D. (1995). On the Bayesian Occam factors argument for Occams razor Computational learning and natural learning systems. 3

Zador A, Deweese M. (1998). Asymmetric dynamics in optimal variance adaptation Neural Computation. 10

Zheng Y, Raftery A. (2003). Discussion: Performance of Bayesian model averaging J Am Stat Assoc. 98

de Ruyter van Steveninck RR, Bialek W. (2005). Features and dimensions: Motion estimation in fly vision Manuscript submitted for publication.