Dreaming of mathematical neuroscience for half a century
暂无分享,去创建一个
[1] S. Amari,et al. Characteristics of Random Nets of Analog Neuron-Like Elements , 1972, IEEE Trans. Syst. Man Cybern..
[2] S. Amari,et al. Singularities Affect Dynamics of Learning in Neuromanifolds , 2006, Neural Computation.
[3] S.-I. Amari,et al. Neural theory of association and concept-formation , 1977, Biological Cybernetics.
[4] Shun-ichi Amari,et al. Measure of Correlation Orthogonal to Change in Firing Rate , 2009, Neural Computation.
[5] Shun-ichi Amari,et al. Dynamics of learning near singularities in radial basis function networks , 2008, Neural Networks.
[6] Shun-ichi Amari,et al. Statistical neurodynamics of associative memory , 1988, Neural Networks.
[7] A. A. Mullin,et al. Principles of neurodynamics , 1962 .
[8] Shun-ichi Amari,et al. Four Types of Learning Curves , 1992, Neural Computation.
[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[10] Anthony C. C. Coolen,et al. Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks , 1998, NIPS.
[11] Shun-ichi Amari,et al. A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..
[12] Shun-ichi Amari,et al. A method of statistical neurodynamics , 1974, Kybernetik.
[13] Shun-ichi Amari,et al. Stochastic Reasoning, Free Energy, and Information Geometry , 2004, Neural Computation.
[14] S. Amari,et al. A Mathematical Foundation for Statistical Neurodynamics , 1977 .
[15] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[16] Marc Timme,et al. Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity , 2011, Front. Comput. Neurosci..
[17] Shun-ichi Amari,et al. Characteristics of randomly connected threshold-element networks and network systems , 1971 .
[18] Masato Okada,et al. A hierarchy of macrodynamical equations for associative memory , 1995, Neural Networks.
[19] J. Cowan,et al. A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.
[20] Shun-ichi Amari,et al. Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements , 1972, IEEE Transactions on Computers.
[21] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[22] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[23] J. Cowan,et al. Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.
[24] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[25] Shun-ichi Amari,et al. Information geometry on hierarchy of probability distributions , 2001, IEEE Trans. Inf. Theory.
[26] Roman Bek,et al. Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.
[27] Masato Okada,et al. Estimating Spiking Irregularities Under Changing Environments , 2006, Neural Computation.
[28] Yutaka Sakai,et al. Synchronous Firing and Higher-Order Interactions in Neuron Pool , 2003, Neural Computation.
[29] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[30] C. Malsburg,et al. How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[31] S. Amari,et al. Formation of topographic maps and columnar microstructures in nerve fields , 1979, Biological Cybernetics.
[32] Shun-ichi Amari,et al. A universal theorem on learning curves , 1993, Neural Networks.
[33] S. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.
[34] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[35] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[36] S. Amari,et al. State concentration exponent as a measure of quickness in Kauffman-type networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[37] Shun-ichi Amari,et al. State concentration measure of quickness in Kauffman-type networks , 2012 .
[38] C. von der Malsburg. Self-organization of orientation sensitive cells in the striate cortex. , 1973, Kybernetik.
[39] Shun-ichi Amari,et al. Information-Geometric Measure for Neural Spikes , 2002, Neural Computation.
[40] Shun-ichi Amari,et al. Blind source separation-semiparametric statistical approach , 1997, IEEE Trans. Signal Process..
[41] Takafumi Kanamori,et al. Information Geometry of U-Boost and Bregman Divergence , 2004, Neural Computation.
[42] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[43] Shun-ichi Amari,et al. Mathematical foundations of neurocomputing , 1990, Proc. IEEE.
[44] Sherrington,et al. Dynamics of fully connected attractor neural networks near saturation. , 1993, Physical review letters.
[45] Heskes,et al. Learning processes in neural networks. , 1991, Physical review. A, Atomic, molecular, and optical physics.
[46] Masato Okada,et al. Information Loss Associated with Imperfect Observation and Mismatched Decoding , 2011, Front. Comput. Neurosci..
[47] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .
[48] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[49] Shun-ichi Amari,et al. Information geometry of Boltzmann machines , 1992, IEEE Trans. Neural Networks.