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108,020
Information geometric measure for neural spikes
 Neural Computation
, 2002
"... The present study introduces informationgeometric measures to analyze neural ring patterns by taking not only the secondorder but also higherorder interactions among neurons into account. Information geometry provides useful tools and concepts for this purpose, including the orthogonality of coo ..."
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Cited by 14 (5 self)
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The present study introduces informationgeometric measures to analyze neural ring patterns by taking not only the secondorder but also higherorder interactions among neurons into account. Information geometry provides useful tools and concepts for this purpose, including the orthogonality
InformationGeometric Decomposition in Spike Analysis
 Diettrich, S. Becker, Z. Ghahramani (Eds.), NIPS
, 2001
"... We present an informationgeometric measure to systematically investigate neuronal firing patterns, taking account not only of the secondorder but also of higherorder interactions. We begin with the case of two neurons for illustration and show how to test whether or not any pairwise correlati ..."
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Cited by 7 (3 self)
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We present an informationgeometric measure to systematically investigate neuronal firing patterns, taking account not only of the secondorder but also of higherorder interactions. We begin with the case of two neurons for illustration and show how to test whether or not any pairwise
Examples of Applications of InformationGeometric Measure to Neural Data
, 2002
"... This document summarizes the examples that illuminate the merits and applicability of the method proposed in (Nakahara and Amari, accepted), using artificially simulated data as well as experimental data from the prefrontal and dorsal extrastriate visual cortices of monkeys (Anderson et al., 1999 ..."
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This document summarizes the examples that illuminate the merits and applicability of the method proposed in (Nakahara and Amari, accepted), using artificially simulated data as well as experimental data from the prefrontal and dorsal extrastriate visual cortices of monkeys (Anderson et al., 1999). Some examples are the same as in (Nakahara and Amari, accepted), while new examples, particularly including experimental data and the case of autocorrelation, are included. Thus, this document should be read as a companion of (Nakahara and Amari, accepted).
inferences in the neural spike train space
"... An informationgeometric framework for statistical ..."
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 614 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
Survey on Independent Component Analysis
 NEURAL COMPUTING SURVEYS
, 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 2241 (104 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation
Complete discrete 2D Gabor transforms by neural networks for image analysis and compression
, 1988
"... AbstractA threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor ” representations for image analysis, segmentation, and compression. These transforms are conjoint spatiahpectral representations [lo], [15], which provide ..."
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Cited by 475 (8 self)
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AbstractA threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor ” representations for image analysis, segmentation, and compression. These transforms are conjoint spatiahpectral representations [lo], [15], which
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 728 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear
Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of Their Cleavage Sites
, 1997
"... We have developed a new method for identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied on genomewide ..."
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Cited by 766 (17 self)
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We have developed a new method for identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied on genome
Results 1  10
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108,020