36 research outputs found
Bifurcation analysis in an associative memory model
We previously reported the chaos induced by the frustration of interaction in
a non-monotonic sequential associative memory model, and showed the chaotic
behaviors at absolute zero. We have now analyzed bifurcation in a stochastic
system, namely a finite-temperature model of the non-monotonic sequential
associative memory model. We derived order-parameter equations from the
stochastic microscopic equations. Two-parameter bifurcation diagrams obtained
from those equations show the coexistence of attractors, which do not appear at
absolute zero, and the disappearance of chaos due to the temperature effect.Comment: 19 page
Application of two-parameter dynamical replica theory to retrieval dynamics of associative memory with non-monotonic neurons
The two-parameter dynamical replica theory (2-DRT) is applied to investigate
retrieval properties of non-monotonic associative memory, a model which lacks
thermodynamic potential functions. 2-DRT reproduces dynamical properties of the
model quite well, including the capacity and basin of attraction.
Superretrieval state is also discussed in the framework of 2-DRT. The local
stability condition of the superretrieval state is given, which provides a
better estimate of the region in which superretrieval is observed
experimentally than the self-consistent signal-to-noise analysis (SCSNA) does.Comment: 16 pages, 19 postscript figure
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State estimation for delayed neural networks
Copyright [2005] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this letter, the state estimation problem is studied for neural networks with time-varying delays. The interconnection matrix and the activation functions are assumed to be norm-bounded. The problem addressed is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally exponentially stable. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. In particular, we derive the conditions for the existence of the desired estimators for the delayed neural networks. We also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities (LMIs). Finally, it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks. Numerical examples are included to illustrate the applicability of the proposed design method
Associative memory by virtual oscillator network based on single spin-torque oscillator
A coupled oscillator network may be able to perform an energy-efficient
associative memory operation. However, its realization has been difficult
because inhomogeneities unavoidably arise among the oscillators during
fabrication and lead to an unreliable operation. This issue could be resolved
if the oscillator network were able to be formed from a single oscillator.
Here, we performed numerical simulations and theoretical analyses on an
associative memory operation that uses a virtual oscillator network based on a
spin-torque oscillator. The virtual network combines the concept of coupled
oscillators with that of feedforward neural networks. Numerical experiments
demonstrate successful associations of -pixel patterns with various
memorized patterns. Moreover, the origin of the associative memory is shown to
be forced synchronization driven by feedforward input, where phase differences
among oscillators are fixed and correspond to the colors of the pixels in the
pattern.Comment: 15 pages, 4 figure
The stability and attractivity of neural associative memories.
Han-bing Ji.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (p. 160-163).Microfiche. Ann Arbor, Mich.: UMI, 1998. 2 microfiches ; 11 x 15 cm
Selective retrieval of memory and concept sequences through neuro-windows
This letter presents a crosscorrelational associative memory model which realizes selective retrieval of pattern sequences. When hierarchically correlated sequences are memorized, sequences of the correlational centers can be defined as the concept sequences. The authors propose a modified neuro-window method which enables selective retrieval of memory sequences and concept sequences. It is also shown that the proposed model realizes capacity expansion of the memory which stores random sequences