Free-energy and the brain
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[1] Rajesh P. N. Rao,et al. Bayesian inference and attentional modulation in the visual cortex , 2005, Neuroreport.
[2] P. Goldman-Rakic,et al. Preface: Cerebral Cortex Has Come of Age , 1991 .
[3] P. Cz.. Handbuch der physiologischen Optik , 1896 .
[4] Karl J. Friston,et al. Dynamic representations and generative models of brain function , 2001, Brain Research Bulletin.
[5] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[6] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[7] Daniel A. Levinthal,et al. Exploration and Exploitation in Organizational Learning , 2007 .
[8] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[9] L Krubitzer,et al. Area 3a: topographic organization and cortical connections in marmoset monkeys. , 2001, Cerebral cortex.
[10] Debra J. Searles,et al. The Fluctuation Theorem , 2002 .
[11] Angela J. Yu,et al. Uncertainty, Neuromodulation, and Attention , 2005, Neuron.
[12] Huzihiro Araki,et al. Quantum and Non-Commutative Analysis , 1993 .
[13] John H. R. Maunsell,et al. Attentional modulation of visual motion processing in cortical areas MT and MST , 1996, Nature.
[14] Karl J. Friston. Learning and inference in the brain , 2003, Neural Networks.
[15] R. F. Streater. The Free-energy Theorem , 1993 .
[16] A Prince,et al. Optimality: From Neural Networks to Universal Grammar , 1997, Science.
[17] Christopher C. Pack,et al. Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain , 2001, Nature.
[18] Tomaso Poggio,et al. Computational vision and regularization theory , 1985, Nature.
[19] Paul Schrater,et al. Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[20] G. Edelman. Neural Darwinism: Selection and reentrant signaling in higher brain function , 1993, Neuron.
[21] M. Mesulam,et al. From sensation to cognition. , 1998, Brain : a journal of neurology.
[22] R Linsker,et al. Perceptual neural organization: some approaches based on network models and information theory. , 1990, Annual review of neuroscience.
[23] Panos E. Trahanias,et al. Modelling brain emergent behaviours through coevolution of neural agents , 2006, Neural Networks.
[24] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[25] P. Klouček,et al. The computational modeling of nonequilibrium thermodynamics of the martensitic transformations , 1998 .
[26] B. Efron,et al. Stein's Estimation Rule and Its Competitors- An Empirical Bayes Approach , 1973 .
[27] E Harth,et al. The inversion of sensory processing by feedback pathways: a model of visual cognitive functions. , 1987, Science.
[28] M. Young,et al. Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[29] G. Orban,et al. Laminar distribution of NMDA receptors in cat and monkey visual cortex visualized by [3H]‐MK‐801 binding , 1993, The Journal of comparative neurology.
[30] G. Crooks. Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[31] J. B. Levitt,et al. Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1. , 2002, Progress in brain research.
[32] Shihui Han,et al. Modulation of neural activities by enhanced local selection in the processing of compound stimuli , 2003, Human brain mapping.
[33] J. M. Hupé,et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.
[34] D. Mackay. The Epistemological Problem for Automata , 1956 .
[35] E. M.,et al. Statistical Mechanics , 2021, Manual for Theoretical Chemistry.
[36] Karl J. Friston,et al. DEM: A variational treatment of dynamic systems , 2008, NeuroImage.
[37] D Mumford,et al. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.
[38] Denis J. Evans,et al. A non-equilibrium free energy theorem for deterministic systems , 2003 .
[39] H. Spitzer,et al. Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics. , 1987, Journal of neurophysiology.
[40] C. Gilbert,et al. Synaptic physiology of horizontal connections in the cat's visual cortex , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[41] R. Kass,et al. Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models) , 1989 .
[42] Geoffrey E. Hinton,et al. Parallel visual computation , 1983, Nature.
[43] A. Dale,et al. Human posterior auditory cortex gates novel sounds to consciousness. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[44] S. Shipp,et al. The functional logic of cortical connections , 1988, Nature.
[45] Tomaso Poggio,et al. Computational vision and regularization theory , 1985, Nature.
[46] Mitsuo Kawato,et al. A forward-inverse optics model of reciprocal connections between visual cortical areas , 1993 .
[47] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[48] Pierre Baldi,et al. Bayesian surprise attracts human attention , 2005, Vision Research.
[49] R. Andersen. Visual and eye movement functions of the posterior parietal cortex. , 1989, Annual review of neuroscience.
[50] D A Pollen,et al. On the neural correlates of visual perception. , 1999, Cerebral cortex.
[51] Tai Sing Lee,et al. Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[52] D. Mackay. Free energy minimisation algorithm for decoding and cryptanalysis , 1995 .
[53] D. Mumford. On the computational architecture of the neocortex , 2004, Biological Cybernetics.
[54] J. DeFelipe,et al. Microstructure of the neocortex: Comparative aspects , 2002, Journal of neurocytology.
[55] Karl J. Friston,et al. Variational free energy and the Laplace approximation , 2007, NeuroImage.
[56] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[57] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[58] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[59] Joseph J. Atick,et al. Towards a Theory of Early Visual Processing , 1990, Neural Computation.
[60] P. Földiák,et al. Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.
[61] John J. Foxe,et al. Determinants and mechanisms of attentional modulation of neural processing. , 2001, Frontiers in Bioscience.
[62] R. Guillery,et al. On the actions that one nerve cell can have on another: distinguishing "drivers" from "modulators". , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[63] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[64] L. Optican,et al. Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. , 1987, Journal of neurophysiology.
[65] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[66] A. Clark,et al. Trading spaces: Computation, representation, and the limits of uninformed learning , 1997, Behavioral and Brain Sciences.
[67] S. Treue,et al. Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex , 2004, Current Biology.
[68] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[69] J. B. Levitt,et al. Circuits for Local and Global Signal Integration in Primary Visual Cortex , 2002, The Journal of Neuroscience.
[70] P. C. Murphy,et al. Corticofugal feedback influences the generation of length tuning in the visual pathway , 1987, Nature.
[71] 久保 亮五,et al. H. Haken: Synergetics; An Introduction Non-equilibrium Phase Transitions and Self-Organization in Physics, Chemistry and Biology, Springer-Verlag, Berlin and Heidelberg, 1977, viii+325ページ, 251×17.5cm, 11,520円. , 1978 .
[72] H. Morowitz,et al. Energy Flow in Biology , 1969 .
[73] T. Shallice,et al. Neuroimaging evidence for dissociable forms of repetition priming. , 2000, Science.
[74] W. Singer,et al. In search of common foundations for cortical computation , 1997, Behavioral and Brain Sciences.
[75] S. Hochstein,et al. View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.
[76] Roman Borisyuk,et al. A theory of epineuronal memory , 2004, Neural Networks.
[77] R. Näätänen. Mismatch negativity: clinical research and possible applications. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[78] K. Rockland,et al. Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey , 1979, Brain Research.
[79] A. Yuille,et al. Object perception as Bayesian inference. , 2004, Annual review of psychology.
[80] D. Harville. Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems , 1977 .
[81] Karl J. Friston,et al. A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[82] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.