Coherent Infomax as a Computational Goal for Neural Systems
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[1] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[2] A. Zador. Impact of synaptic unreliability on the information transmitted by spiking neurons. , 1998, Journal of neurophysiology.
[3] Yves Chauvin,et al. Backpropagation: theory, architectures, and applications , 1995 .
[4] Stefano Panzeri,et al. The Upward Bias in Measures of Information Derived from Limited Data Samples , 1995, Neural Computation.
[5] Dario Floreano,et al. Contextually guided unsupervised learning using local multivariate binary processors , 1998, Neural Networks.
[6] Geoffrey E. Hinton,et al. Self-organizing neural network that discovers surfaces in random-dot stereograms , 1992, Nature.
[7] Michael DeWeese,et al. Optimization Principles for the Neural Code , 1995, NIPS.
[8] Suzanna Becker,et al. Mutual information maximization: models of cortical self-organization. , 1996, Network.
[9] S. Kullback,et al. The Information in Contingency Tables , 1980 .
[10] 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.
[11] C. L. Chapman,et al. Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability. , 2001, Bio Systems.
[12] P. Dayan,et al. Space and time in visual context , 2007, Nature Reviews Neuroscience.
[13] B J Craven,et al. Interactions between coincident and orthogonal cues to texture boundaries , 2000, Perception & psychophysics.
[14] D. G. Johnson,et al. The Role and Effectiveness of Theories of Decision in Practice , 1977 .
[15] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[16] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[17] W. Singer,et al. In search of common foundations for cortical computation , 1997, Behavioral and Brain Sciences.
[18] Christopher T. Kello,et al. The emergent coordination of cognitive function. , 2007, Journal of experimental psychology. General.
[19] J Kay,et al. Measures for investigating the contextual modulation of information transmission. , 1996, Network.
[20] W. Singer,et al. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex , 1990, Nature.
[21] R. Traub,et al. Region-specific changes in gamma and beta2 rhythms in NMDA receptor dysfunction models of schizophrenia. , 2008, Schizophrenia bulletin.
[22] A. Norman Redlich,et al. Redundancy Reduction as a Strategy for Unsupervised Learning , 1993, Neural Computation.
[23] S. Kullback,et al. Information Theory and Statistics , 1959 .
[24] Michael W. Spratling,et al. A feedback model of perceptual learning and categorization , 2006, Visual Cognition.
[25] V. Lamme,et al. The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.
[26] Mark D. Plumbley,et al. Information Theory and Neural Networks , 1993 .
[27] Jim Kay,et al. Neural networks for unsupervised learning based on information theory , 2000 .
[28] Felix Creutzig,et al. Predictive Coding and the Slowness Principle: An Information-Theoretic Approach , 2008, Neural Computation.
[29] Michael A. Arbib,et al. The handbook of brain theory and neural networks , 1995, A Bradford book.
[30] D. Lindley. On a Measure of the Information Provided by an Experiment , 1956 .
[31] Gal Chechik,et al. Information Bottleneck for Gaussian Variables , 2003, J. Mach. Learn. Res..
[32] M. Tsukada,et al. Stochastic automaton models for the temporal pattern discrimination of nerve impulse sequences , 1976, Biological Cybernetics.
[33] Barry J. Richmond,et al. Unbiased measures of transmitted information and channel capacity from multivariate neuronal data , 1991, Biological Cybernetics.
[34] S. Finger. Origins of Neuroscience , 1994 .
[35] Michael W. Spratling. Predictive coding as a model of biased competition in visual attention , 2008, Vision Research.
[36] D. M. Titterington,et al. Statistics and Neural Networks , 2000, Technometrics.
[37] D. Lewis,et al. Cortical inhibitory neurons and schizophrenia , 2005, Nature Reviews Neuroscience.
[38] Karl J. Friston. Learning and inference in the brain , 2003, Neural Networks.
[39] William J. McGill. Multivariate information transmission , 1954, Trans. IRE Prof. Group Inf. Theory.
[40] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[41] Joseph J Atick,et al. Could information theory provide an ecological theory of sensory processing? , 2011, Network.
[42] S. Becker. Jpmax: Learning to Recognize Moving Objects as a Model--tting Problem 1 Learning Coherent Classifications , 1995 .
[43] Helen Suzanna Becker,et al. An information-theoretic unsupervised learning algorithm for neural networks , 1993 .
[44] Jim Kay,et al. Activation Functions, Computational Goals, and Learning Rules for Local Processors with Contextual Guidance , 1997, Neural Computation.
[45] J. Aitchison,et al. Principles, practice and performance in decision-making in clinical medicine , 1975 .
[46] T. Sejnowski,et al. Book Review: Gain Modulation in the Central Nervous System: Where Behavior, Neurophysiology, and Computation Meet , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[47] Konrad Paul Kording,et al. Learning with two sites of synaptic integration , 2000, Network.
[48] M. Tsukada,et al. Temporal pattern discrimination of impulse sequences in the computer-simulated nerve cells , 2004, Biological Cybernetics.
[49] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[50] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[51] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[52] Richard W. Hamming,et al. Coding and Information Theory , 1980 .
[53] Ernst Strüngmann Forum,et al. Dynamic coordination in the brain : from neurons to mind , 2010 .
[54] T. Sanger. A Probability Interpretation of Neural Population Coding for Movement , 1997 .
[55] Ralph Linsker,et al. Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.
[56] Jorge V. José,et al. Inhibitory synchrony as a mechanism for attentional gain modulation , 2004, Journal of Physiology-Paris.
[57] Suzanna Becker,et al. Learning to Categorize Objects Using Temporal Coherence , 1992, NIPS.
[58] G. V. van Orden,et al. Dispersion of response times reveals cognitive dynamics. , 2009, Psychological review.
[59] 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.
[60] W. A. Phillips,et al. Where the rubber meets the road: The importance of implementation , 2003, Behavioral and Brain Sciences.
[61] Jim Kay,et al. The discovery of structure by multi-stream networks of local processors with contextual guidance , 1995 .
[62] G. Edelman,et al. A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.
[63] W. R. Garner. Applications of Information Theory to Psychology , 1959 .
[64] J. G. Taylor,et al. Mathematical Approaches to Neural Networks , 1993 .
[65] Miles A Whittington,et al. Interneuron Diversity series: Inhibitory interneurons and network oscillations in vitro , 2003, Trends in Neurosciences.
[66] Minoru Tsukada,et al. Temporal pattern discrimination in the cat's retinal cells and Markov system models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[67] Geoffrey E. Hinton,et al. Spatial coherence as an internal teacher for a neural network , 1995 .