List of Publications Competitive and Neuronal Computation
Wolfgang Banzhaf
Older papers (from 1993 back) are represented by abstracts only
and are available upon email request
We give titles and links. If you click the underlined words in a title
you will see an abstract and source information of the paper. If you click
the corresponding filename you will retrieve a copy.
2021
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2002
1994
1992
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1990
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1987
Some Abstracts and Sources
TITLE: On the Dynamics of Competition in a simple Artificial Chemistry
AUTHORS: Wolfgang Banzhaf
SOURCE: Nonlinear Phenomena in Complex Systems, Vol. 5 (2002) pp. 318 - 324
ABSTRACT:
We examine a simple system of competing and cooperating
entities in terms of the speed of settling their competition.
It turns out that the larger the degree of cooperativity
among entities the quicker the competition is decided. This result,
derived in a simple artificial chemistry system, demonstrates that
cooperativity is a decisive element of a world of entities competing
for resources. It also hints at the fact that growth of complexity
(in terms of increasing cooperativity) is a native tendency
of such a world.
FILENAME:
npcs.pdf
(180 kB)
TITLE: Optical Implementation of a Competitive Network
AUTHORS: W. Banzhaf, E. Lange, M. Oita, J. Ohta, K. Kyuma and T. Nakayama
EDITORS: V.L. Plantamura, B. Soucek, G. Visaggio
ABSTRACT:
FILENAME:
TITLE: A Dynamical Implementation of Self-organizing Maps
AUTHORS: Wolfgang Banzhaf and Manfred Schmutz
SOURCE: Proceedings 1st. Int. Conf. on Applied Synergetics and Synergetic
Engineering (ICASSE-94), Erlangen, F.G. B\"obel, T. Wagner (Eds.), Fraunhofer
Institut IIE, 1994, pp. 66 --- 73
ABSTRACT: The standard learning algorithm for self-organizing maps (SOM)
involves the two steps of a search for the best matching neuron and of
an update of its weight vectors in the neighborhood of this neuron. In
the dynamical implementation discussed here, a competitive dynamics of
laterally coupled neurons with diffusive interaction is used to find the
best-matching neuron. The resulting neuronal excitation bubbles are used
to drive a Hebbian learning algorithm that is similar to the one Kohonen
uses. Convergence of the SOM is achieved here by relating time (or number
of training steps) to the strength of the diffusive coupling. A standard
application of the SOM is used to demonstrate the feasibility of the approach.
TITLE: Competition as an organizational principle for massively parallel
computers?
AUTHORS: Wolfgang Banzhaf
SOURCE: Proceedings of the Workshop on, Physics and Computation, Dallas,
TX, 1992, IEEE Computer Society Press, Los Alamitos, pp. 229 --- 231
ABSTRACT: We discuss the idea of using competition as a guiding principle
for organizing a parallel computer. We argue that competitive interactions
are ubiquous in many systems and deserve to be looked at in parallel computing.
We outline some relevant questions which have to be answered in this context.
TITLE: Some Notes on Competition among Cell Assemblies
AUTHORS: Wolfgang Banzhaf and Manfred Schmutz
SOURCE: International Journal on Neural Systems, Volume 2 (1992), pp. 303
--- 313
ABSTRACT: We discuss a family of competitive dynamics useful for pattern
recognition purposes. Derived from a physical model of mode competition,
they generalize former concepts to include populations of cells working
as grandmother cell assemblies. Also the notion of unfair competition is
introduced.
TITLE: Learning in a competitive network
AUTHORS: Wolfgang Banzhaf and Hermann Haken
SOURCE: Neural Networks, Volume 3 (1990), pp. 423 --- 435
ABSTRACT: We consider the abilities of a recently published neural network
model to recognize and classify arbitrary patterns. We introduce a learning
scheme based on Hebb's rule which allows the system's neuronal cells to
specialize on different patterns during learning. The rule which was originally
introduced by Kohonen is appropriately modified and applied to the competitive
network under study. A variant of the learning dynamics is then derived
from an energy functional characterizing the specialization state of the
network. Simulations are presented to demonstrate the specialization process
for different pattern distributions.
FILENAME: To obtain it, please send me an email!
Wolfgang Banzhaf
Last updated: Sep 10, 2021