Journal ArticleDOI
Representation of Associated Data by Matrix Operators
Teuvo Kohonen,Matti Ruohonen +1 more
TLDR
It is shown that associated pairs of vectoral items can be recorded by transforming them into a matrix operator M so that a particular stored vector X(r) can be reproduced by multiplying an associated cue vector Q( r) by M.Abstract:
It is shown that associated pairs of vectoral items (Q(r), X(r)) can be recorded by transforming them into a matrix operator M so that a particular stored vector X(r) can be reproduced by multiplying an associated cue vector Q(r) by M. If the number of pairs does not exceed the dimension of the cue and all cue vectors are linearly independent, then the recollections are perfect replicas of the recorded items and there will be no crosstalk from the other recorded items. If these conditions are not valid, the recollections are still linear least square approximations of the X(r). The relationship of these mappings to linear estimators is discussed. These transforms can be readily implemented by linear analog systems.read more
Citations
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Journal ArticleDOI
Statistical neurodynamics of associative memory
Shun-ichi Amari,Kenjiro Maginu +1 more
TL;DR: A new statistical neurodynamical method is proposed for analyzing the non-equilibrium dynamical behaviors of an autocorrelation associative memory model and explains the strange behaviors due to strange shapes of the basins of attractors.
Journal ArticleDOI
Neural network models for pattern recognition and associative memory
TL;DR: This review outlines some fundamental neural network modules for associative memory, pattern recognition, and category learning andAdaptive filter formalism provides a unified notation.
BookDOI
Disordered Systems and Biological Organization
TL;DR: The present introduction outlInes the relationships between the contributions presented at the NATO workshop on Disordered Systems and Biological Organization and discusses each paper in its particular scientific context.
Book ChapterDOI
Learning Process in an Asymmetric Threshold Network
TL;DR: A learning procedure which requires the outside world to specify the state of every neuron during the learning session can hardly be considered as a general learning rule because in real-world conditions, only a partial information on the “ideal” network state for each task is available from the environment.
Journal ArticleDOI
On optimal nonlinear associative recall.
TL;DR: The problem of determining the nonlinear function (“blackbox”) which optimally associates two sets of data is considered and an iteration method based on the concept of the generalized inverse of a matrix provides the polynomial mapping of degreek onX by whichY is retrieved in an optimal way in the least squares sense.
References
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Journal ArticleDOI
A Generalized inverse for matrices
TL;DR: A generalization of the inverse of a non-singular matrix is described in this paper as the unique solution of a certain set of equations, which is used here for solving linear matrix equations, and for finding an expression for the principal idempotent elements of a matrix.
Journal ArticleDOI
Non-holographic associative memory
TL;DR: The features of a hologram that commend it as a model of associative memory can be improved on by other devices.
Book
Correlation matrix memories
TL;DR: In this article, a new model for associative memory based on a correlation matrix is proposed, which is failure tolerant and facilitates associative search of information; these are properties that are usually assigned to holographic memories.
Journal ArticleDOI
Correlation Matrix Memories
TL;DR: A new model for associative memory, based on a correlation matrix, is suggested, in which any part of the memorized information can be used as a key and the memories are selective with respect to accumulated data.
Journal ArticleDOI
On best approximate solutions of linear matrix equations
TL;DR: In this paper, it was shown how to define a generalized inverse of a non-singular matrix, which has relevance to the statistical problem of finding the best approximate solution of inconsistent systems of equations by the method of least squares.