Oscillatory Neural Networks

Author: Margarita G. Kuzmina
Publisher: Walter de Gruyter
ISBN: 3110269201
Size: 66.46 MB
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This makes the book highly interesting to researchers dealing with various aspects of parallel information processing.

Artificial Neural Networks Icann 96

Author: Christoph von der Malsburg
Publisher: Springer Science & Business Media
ISBN: 9783540615101
Size: 76.71 MB
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6th International Conference, Bochum, Germany, July 16 - 19, 1996. Proceedings
Christoph von der Malsburg. A Local Connected Neural Oscillator Network for
Pattern Segmentation Hiroaki Kurokawa and Shinsaku Mori Dept. of E.E. Keio
University 3-14-1 Kouhoku-ku Hiyoshi Yokohama 223 JAPAN ...

Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering

Author: Nikola K. Kasabov
Publisher: Marcel Alencar
ISBN: 0262112124
Size: 80.94 MB
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The SOM neural network is chaotic. Chaos introduces a controlled randomness
into the self-organizing maps, which, according to the authors, results in an
improved ability for clustering. Further development of the idea of building chaotic
neurons and using them for modeling the chaotic behavior of large ensembles of
neurons leads to oscillatory neurons and oscillatory neural networks. 7.3.3
Oscillatory Neurons and Oscillatory Neurocomputers Oscillatory models of
different brain ...

The Handbook Of Brain Theory And Neural Networks

Author: Michael A. Arbib
Publisher: MIT Press
ISBN: 0262011972
Size: 75.61 MB
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if 'i ^,,i i* C" denotes a panem of phase relations between neurons to be
memorized isee also HEH, BlAN SYNAPT1C PLAST1C1TYI, Notice that the
problem of negative imirrori images does not arise in oscillatory neural networks.
since both .?' and - j'* result in the same phase relations. The key difference
between the Hopficld,Grossberti network and the oscillatory network iEquation 3i
is that memonzed images correspond to equilibrnim ipointi attraclors in the
former and to limit cycle ...

Weakly Connected Neural Networks

Author: Frank C. Hoppensteadt
Publisher: Springer Science & Business Media
ISBN: 1461218284
Size: 36.33 MB
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Without loss of generality we may assume that the entire network is one such
pool. For the sake of simplicity we assume that these oscillators have equal
natural frequencies Q1 = . . . = Qn. If we use slow time T = et and discard high-
order terms in 8, then the canonical model (9.50) can be written in the form (9.56).
The following theorem shows that under some conditions, Oscillatory neural
network (9.56) can have interesting neurocomputational properties. In particular,
it can act as a ...

Artificial Neural Networks Icann 2008

Author: Vera Kurkova-Pohlova
Publisher: Springer
ISBN: 354087559X
Size: 33.35 MB
Format: PDF, Mobi
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winner-take-all procedure and is implemented through a proper modification of
the weights of bottom-up and top-down connections in a hierarchical neural
network. It works well in solving particular tasks but it is incompatible with modern
hypothesis about distributed representation of visual information in the brain [5,6].
There are many experimental evidences showing the importance of neural
synchronization and oscillation in various brain functions. Neural oscillations in
the gamma ...

Oscillations In Neural Systems

Author: Daniel S. Levine
Publisher: Psychology Press
ISBN: 1135691908
Size: 54.10 MB
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183–199). Boston: Birkäuser. Freeman, W.J. (1994). Qualitative overview of
population neurodynamics. In F.Ventriglia (Ed.), Neural Modeling and Neural
Networks (pp. 185–215). New York: Pergamon. Getting, P.A. (1989). Emerging
principles governing the operation of neural networks. Annual Review of
Neuroscience, 12, 185–204. Ghose, G.M., & Freeman, R.D. (1992). Oscillatory
discharge in the visual system: Does it have a function? Journal of
Neurophysiology, 68, 1558–1574.

Neural Networks From Biology To High Energy Physics Proceedings Of The Third Workshop

Author: Amit Daniel J
Publisher: World Scientific
ISBN: 9814548405
Size: 68.40 MB
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This computational ability of oscillator neural networks is ensured by the fact that
reduced dynamical equations for phase variables in general involve two terms
which are respectively responsible for synchronization and cessation of
oscillations. An approximate theory based on the self-consistent signal-to-noise
analysis well accounts for the equilibrium properties of the phase rotator network
with the quasifixed-point attractors. 1. Introduction Synchronization of oscillatory
neural ...

Models Of Neural Networks

Author: Eytan Domany
Publisher: Springer Science & Business Media
ISBN: 1461243203
Size: 75.78 MB
Format: PDF, Mobi
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U.S.A. 81:3088– 3092 Horn D, Sagi D (1991) Parallel activation of memories in
an oscillatory neural network. Neural Comput. 3:31–43 Horn D, Sagi D, Usher M (
1991) Segmentation, binding, and illusory conjunctions. Neural Comput. 3:510–
525 Horn D, Usher M (1989) Neural networks with dynamical thresholds. Phys.
Rev. A 40:1036–1040 Horn D, Usher M (1992) Oscillatory model of short-term
memory. In: Advances in Neural Information Processing Systems 4, J.E. Moody et