Citation Relationships

Legends: Link to a Model Reference cited by multiple papers


Nemenman I (2005) Fluctuation-dissipation theorem and models of learning. Neural Comput 17:2006-33 [PubMed]

References and models cited by this paper

References and models that cite this paper

Aida T (1999) Field theoretical analysis of on-line learning of probability distributions Phys Rev Lett 83:3554-3557
Atick J (1992) Could information theory provide an ecological theory of sensory processing? Princeton Lectures On Biophysics, Bialek W, ed. pp.223
ATTNEAVE F (1954) Some informational aspects of visual perception. Psychol Rev 61:183-93 [PubMed]
Atwal G, Bialek W (2004) Ambiguous model learning made unambiguous with 1-f, priors Advances in neural information processing systems, Thrun S:Saul L:Scholkopf B, ed.
Balasubramanian V (1997) Statistical inference, Occam's razor, and statistical mechanics on the space of probability distributions Neural Comput 9:349-368
Barlow H (1959) Sensory mechanism, the reduction of redundancy, and intelligence National Physical Laboratory Symposium N. 10 The Mechanization of Thought Processes
Barlow HB (1961) Possible principles underlying the transformations of sensory messages Sensory Communication, Rosenblith WA, ed. pp.217
Bernardo J (2003) Bayesian statistics UNESCO Encyclopedia of Life Support Systems (EOLSS)
Bialek W, de Ruyter van Steveninck RR (2005) Features and dimensions: Motion estimation in fly vision Manuscript submitted for publication
Bialek W, Nemenman I, Tishby N (2001) Predictability, complexity, and learning. Neural Comput 13:2409-63
Brenner N, Bialek W, de Ruyter van Steveninck R (2000) Adaptive rescaling maximizes information transmission. Neuron 26:695-702 [PubMed]
Clarke BS, Barron AR (1990) Information theoretic asymptotics of Bayes methods IEEE Transactions On Information Theory 36:453-471
Cover TM, Thomas JA (1991) Elements of Information Theory
Csiszar I (1975) I-divergence geometry of probability distributions and minimization problems Annals Of Probability 3:146-158
Cucker F, Smale S (2001) On the mathematical foundations of learning Bull Amer Math Soc 39:1-49
Dawid A (1984) Present position and potential developments: Some personal views. Statistical theory: The prequential approach J Roy Stat Soc A 147:278-292
Deweese M, Zador A (1998) Asymmetric dynamics in optimal variance adaptation Neural Computation 10:1179-1202
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. J Exp Psychol Anim Behav Process 27:354-72 [PubMed]
Hall P, Hannan E (1988) On stochastic complexity and nonparametric density estimation Biometrika 75:705-714
Holy T (1997) Analysis of data from continuous probability distributions Phys Rev Lett 79:3545-3548
James W, Stein C (1961) Estimation with quadratic loss Proc Fourth Berkeley Symposium Mathematical Statistics And Probability, Neyman J, ed. pp.361
Janes E (1979) Inference, method, and decision: Towards a Bayesian philosophy of science J Amer Stat Assoc 74:740-741
Jeffreys H (1936) Further significance tests Proc Camb Phil Soc 32:416-445
Lemm J (2002) Bayesian field theory
Ma S (1985) Statistical mechanics
Mackay D (1992) Bayesian Interpolation Neural Comput 4:415-448
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
Raftery A, Zheng Y (2003) Discussion: Performance of Bayesian model averaging J Am Stat Assoc 98:931-938
Rao RP (2004) Bayesian computation in recurrent neural circuits. Neural Comput 16:1-38 [PubMed]
Reichardt W (1961) Autocorrelation: A principle for the evaluation of sensory information by the nervous system Sensory Communication, Rosenblith WA, ed.
Rissanen J (1989) Stochastic Complexity Statistical Inquiry
Rissanen J, Speed T, Yu B (1992) Density estimation by stochastic complexity IEEE Trans Inform Theory 38:315-323
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 6:461-464
Seung HS (2003) Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40:1063-73 [PubMed]
Stone M (1977) An asymptotic equivalence of choice of model by cross-validation and Akaikes criterion J Roy Stat Soc B 39:44-47
Vapnik V (1998) Statistical Learning Theory
Weiss TF (1995) Cellular Biophysics Electrical Properties
Wolpert D (1995) On the Bayesian Occam factors argument for Occams razor Computational learning and natural learning systems, Petsche T, ed.
(41 refs)