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Pattern Recognition in Bioinformatics - 9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings (Paperback, 2014 ed.)
Matteo Comin, Lukas Kall, Elena Marchiori, Alioune Ngom, Jagath Chandana Rajapakse
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This book constitutes the refereed proceedings of the 8th IAPR
International Conference on Pattern Recognition in Bioinformatics,
PRIB 2014, held in Stockholm, Sweden in August 2014. The 9 revised
full papers and 9 revised short papers presented were carefully
reviewed and selected from 29 submissions. The focus of the
conference was on the latest Research in Pattern Recognition and
Computational Intelligence-Based Techniques Applied to Problems in
Bioinformatics and Computational Biology.
The field of neural information processing has two main objects:
investigation into the functioning of biological neural networks
and use of artificial neural networks to sol ve real world
problems. Even before the reincarnation of the field of artificial
neural networks in mid nineteen eighties, researchers have
attempted to explore the engineering of human brain function. After
the reincarnation, we have seen an emergence of a large number of
neural network models and their successful applications to solve
real world problems. This volume presents a collection of recent
research and developments in the field of neural information
processing. The book is organized in three Parts, i.e., (1)
architectures, (2) learning algorithms, and (3) applications.
Artificial neural networks consist of simple processing elements
called neurons, which are connected by weights. The number of
neurons and how they are connected to each other defines the
architecture of a particular neural network. Part 1 of the book has
nine chapters, demonstrating some of recent neural network
architectures derived either to mimic aspects of human brain
function or applied in some real world problems. Muresan provides a
simple neural network model, based on spiking neurons that make use
of shunting inhibition, which is capable of resisting small scale
changes of stimulus. Hoshino and Zheng simulate a neural network of
the auditory cortex to investigate neural basis for encoding and
perception of vowel sounds.
The field of neural information processing has two main objects:
investigation into the functioning of biological neural networks
and use of artificial neural networks to sol ve real world
problems. Even before the reincarnation of the field of artificial
neural networks in mid nineteen eighties, researchers have
attempted to explore the engineering of human brain function. After
the reincarnation, we have seen an emergence of a large number of
neural network models and their successful applications to solve
real world problems. This volume presents a collection of recent
research and developments in the field of neural information
processing. The book is organized in three Parts, i.e., (1)
architectures, (2) learning algorithms, and (3) applications.
Artificial neural networks consist of simple processing elements
called neurons, which are connected by weights. The number of
neurons and how they are connected to each other defines the
architecture of a particular neural network. Part 1 of the book has
nine chapters, demonstrating some of recent neural network
architectures derived either to mimic aspects of human brain
function or applied in some real world problems. Muresan provides a
simple neural network model, based on spiking neurons that make use
of shunting inhibition, which is capable of resisting small scale
changes of stimulus. Hoshino and Zheng simulate a neural network of
the auditory cortex to investigate neural basis for encoding and
perception of vowel sounds.
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