Advancing artificial intelligence through biological process applications:
"This book presents recent advancements in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, it will be...
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| Format: | Book |
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| Language: | English |
| Published: |
Hershey, PA
Medical Information Science Reference
2009
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| Online Access: | Inhaltsverzeichnis |
| Summary: | "This book presents recent advancements in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, it will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres"--Provided by publisher |
| Item Description: | "Premier reference source"--Cover Includes bibliographical references (p. 395-421) and index |
| Physical Description: | XXII, 436 S. Ill. 29 cm |
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| 245 | 1 | 0 | |a Advancing artificial intelligence through biological process applications |c Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta, [eds.] |
| 264 | 1 | |a Hershey, PA |b Medical Information Science Reference |c 2009 | |
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| 500 | |a Includes bibliographical references (p. 395-421) and index | ||
| 520 | |a "This book presents recent advancements in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, it will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres"--Provided by publisher | ||
| 650 | 4 | |a Artificial intelligence / Medical applications | |
| 650 | 4 | |a Artificial intelligence / Biological applications | |
| 650 | 4 | |a Neural networks (Neurobiology) | |
| 650 | 4 | |a Künstliche Intelligenz | |
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Record in the Search Index
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| adam_text | Table of Contents
Foreword xiii
Preface xv
Section I
Recent Advances in Biological Processes Related to Information Processing
Chapter I
Corticofugal Modulation of Tactile Responses of Neurons in the Spinal Trigetninal Nucleus:
A Wavelet Coherence Study I
Eduardo Malmierca, Universidad Autonoma de Madrid, Spain
Nazareth P. Castellanos, Universidad Complutense de Madrid, Spain
ValeriA. Makarov, Universidad Complutense de Madrid, Spain
Angel Nunez, Universidad Autonoma de Madrid, Spain
Chapter II
Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired
Autonomous Systems 20
Didier Le Ray, Universite de Bordeaux, Lab, MAC, France
Morgane Le Bon-Jego, Universite de Bordeaux, Lab, MAC, France
Daniel Cattaert, Universite de Bordeaux, CNIC, Franc
Chapter III
Forward Dendric Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and
Shifting Output Codes 42
Oscar Herreras, Cajal Institute, Spain
Julia Makarova, Cajal Institute, Spain
Jose Manuel Ibarz, Hospital Ramon y Cajal, Spain
Section II
New Biologically Inspired Artificial Intelligence Models
Chapter IV
Spiking Neural P Systems: An Overview 60
Gheorghe Paun, Institute of Mathematics of the Romanian Academy, Romania
Mario J. Perez-Jimenez, University ofSevilla, Spain
Chapter V
Simulation of the Action Potential in the Neuron s Membrane in Artificial Neural Networks 74
Juan Ramon Rabunal Dopico, University ofCoruna, Spain
Javier Pereira Loureiro, University ofCoruna, Spain
Monica Miguelez Rico, University ofCoruna, Spain
Chapter VI
Recent Methodology in Connectionist Systems 94
Ana B. Porto Pazos, University of A Coruna, Spain
Alberto Alvarellos Gonzalez, University of A Coruna, Spain
Alejandro Pazos Sierra, University of A Coruna, Spain
Chapter VII
A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in
Evolutionary Robotics 107
Jose A. Fernandez-Leon, University of Sussex, UK CONICET, Argentina
Gerardo G. Acosta, Univ. Nac. del Centro de la Prov. de Buenos Aires CONICET,
Argentina
Miguel A. Mayosky, Univ. Nac. de la Plata CICPBA, Argentina
Oscar C. Ibdnez, Universitat de les Illes Balears, Palma de Mallorca, Spain
Chapter VIII
An Approach to Artificial Concept Learning Based on Human Concept Learning by
Using Artificial Neural Networks 130
Enrique Merida-Casermeiro, University of Malaga, Spain
Domingo Lopez-Rodriguez, University of Malaga, Spain
J.M. Ortiz-de-Lazcano-Lobato, University of Malaga, Spain
Chapter IX
Artificial Cell Systems Based in Gene Expression Protein Effects 146
Enrique Fernandez-Bianco, University of A Coruna, Spain
Julian Dorado, University of A Coruna, Spain
Nieves Pedreira, University of A Coruna, Spain
Chapter X
Computing vs. Genetics 165
Jose M. Barreiro, Universidad Politecnica de Madrid, Spain
Juan Pazos, Universidad Politecnica de Madrid, Spain
Chapter XI
Artificial Mind for Virtual Characters 182
lara Moema Oberg Vilela, Universidade Federal do Rio de Janeiro, Brazil
Chapter XII
A General Rhythmic Pattern Generation Architecture for Legged Locomotion 202
Zhijun Yan, Stirling University, UK
Felipe M.G. Franca, Universidade Federal do Rio de Janeiro, Brazil
Section III
Real-Life Applications with Biologically Inspired Models
Chapter XIII
Genetic Algorithms and Multimodal Search 231
Marcos Gestal, University of A Coruna, Spain
Jose Manuel Vazquez Naya, University of A Coruna, Spain
Norberto Ezquerra, Georgia Institute of Technology, USA
Chapter XIV
Bimolecular Computing Devices in Synthetic Biology 250
Jesus M. Miro, Universidad Politecnica de Madrid (UPM), Spain
Alfonso Rodriguez-Paton, Universidad Politecnica de Madrid (UPM), Spain
Chapter XV
Guiding Self-Organization in Systems of Cooperative Mobile Agents 268
Alejandro Rodriguez, University of Maryland, USA
Alexander Grushin, University of Maryland, USA
James A. Reggia, University of Maryland, USA
Chapter XVI
Evaluating a Bio-Inspired Approach for the Design of a Grid Information System:
The SO-Grid Portal 291
Agostino Forestiero, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Carlo Mastroianni, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Fausto Pupo, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Giandomenico Spezzano, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Chapter XVII
Graph Based Evolutionary Algorithms 311
Steven M. Corns, Iowa State University, USA
Daniel A. Ashlock, University ofGuelph, Canada
Kenneth Mark Bryden, Iowa State University, USA
Chapter XVIII
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria 331
Daniela Danciu, University of Craiova, Romania
Chapter XIX
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal
Dispersion Coefficient in Rivers 358
Jianhua Yang, University of Warwick, UK
Evor L. Hines, University of Warwick, UK
lanGuymer, University of Warwick, UK
Daciana D. Iliescu, University of Warwick, UK
MarkS. Leeson, University of Warwick, UK
Gregory P. King, University of Warwick, UK
XuQin Li, University of Warwick, UK
Chapter XX
The Exposition of Fuzzy Decision Trees and Their Application in Biology 375
Malcolm J. Beynon, Cardiff University, UK
Kir sty Park, University of Stirling, UK
Compilation of References 395
About the Contributors 422
Index 434
Detailed Table of Contents
Foreword xiii
Preface xv
Section 1
Recent Advances in Biological Processes Related to Information Processing
Chapter I
Corticofugal Modulation of Tactile Responses of Neurons in the Spinal Trigeminal Nucleus:
A Wavelet Coherence Study I
Eduardo Malmierca, Universidad Autonoma de Madrid, Spain
Nazareth P. Castellanos, Universidad Compluteme de Madrid, Spain
Valeri A. Makarov, Universidad Complutense de Madrid, Spain
Angel Nunez, Universidad Autonoma de Madrid, Spain
Recent electrophysiological studies indicate the existence of an important somatosensory processing in
the trigeminal nucleus which is modulated by the corticofugal projection from the somatosensory cortex.
This chapter studies a new mathematical analysis of the temporal structure of neuronal responses during
tactile stimulation of the spinal trigeminal nucleus.
Chapter II
Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired
Autonomous Systems 20
Didier Le Ray, Universite de Bordeaux, Lab, MAC, France
Morgane Le Bon-Jego, Universite de Bordeaux, Lab, MAC, France
Daniel Cattaert, Universite de Bordeaux, CN1C, Franc
Knowledge in invertebrate neuroethology has demonstrated unique advantages for engineering bio¬
logically-based autonomous systems. This chapter aims at presenting some basic neuronal mechanisms
involved in crayfish walking and postural control involving a single key joint of the leg. Due to its rela¬
tive simplicity, the neuronal network responsible for these motor functions is a suitable model for under¬
standing how sensory and motor components interact in the elaboration of appropriate movement and,
therefore, for providing basic principles essential to the design of autonomous embodied systems.
Chapter III
Forward Dendric Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and
Shifting Output Codes 42
Oscar Herreras, Cajal Institute, Spain
Julia Makarova, Cajal Institute, Spain
Jose Manuel Ibarz, Hospital Ramon y Cajal, Spain
This chapter reviews the underlying mechanisms and theoretical implications of the role of voltage-de¬
pendent dendritic currents on the forward transmission of synaptic inputs. The notion analysed brakes
with the classic view of neurons as the elementary units of the brain and attributes them computational/
storage capabilities earlier billed to complex brain circuits.
Section II
New Biologically Inspired Artificial Intelligence Models
Chapter IV
Spiking Neural P Systems: An Overview 60
Gheorghe Paun, Institute of Mathematics of the Romanian Academy, Romania
Mario J. Perez-Jimenez, University ofSevilla, Spain
This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was
recently introduced with motivation from neural computing based on spiking.
Chapter V
Simulation of the Action Potential in the Neuron s Membrane in Artificial Neural Networks 74
Juan Ramon Rabuhal Dopico, University ofCoruna, Spain
Javier Pereira Loureiro, University ofCoruna, Spain
Monica Miguelez Rico, University ofCoruna, Spain
This chapter presents an evolution of the Recurrent ANN (RANN) to enforce the persistence of activa¬
tions within the neurons to create activation contexts that generate correct outputs through time. The
aim of this work is to develop a process element model with activation output much more similar to the
biological neurons one.
Chapter VI
Recent Methodology in Connectionist Systems 94
Ana B. Porto Pazos, University of A Coruna, Spain
Alberto Alvarellos Gonzalez, University of A Coruna, Spain
Alejandro Pazos Sierra, University of A Coruna, Spain
This chapter presents an evolution of the Recurrent ANN (RANN) to enforce the persistence of activa¬
tions within the neurons to create activation contexts that generate correct outputs through time. The
aim of this work is to develop a process element model with activation output much more similar to the
biological neurons one.
Chapter VII
A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in
Evolutionary Robotics 107
Jose A. Fernandez-Leon, University of Sussex, UK CONICET, Argentina
Gerardo G. Acosta, Univ. Nac. del Centro de la Prov. de Buenos Aires CONICET,
Argentina
Miguel A. Mayosky, Univ. Nac. de la Plata CICPBA, Argentina
Oscar C. Ibdnez, Universitat de les Illes Balears, Palma de Mallorca, Spain
This chapter presents the experience gained when developing the path generation modules of autono¬
mous robots, starting with traditional artificial intelligence approaches and ending with the most recent
techniques of Evolutionary Robotics. Discussions around the features and suitability of each technique,
with special interest on immune-based behaviour coordination are proposed to meet the corresponding
theoretical arguments supported by empirical experiences.
Chapter VIII
An Approach to Artificial Concept Learning Based on Human Concept Learning by
Using Artificial Neural Networks 130
Enrique Merida-Casermeiro, University of Malaga, Spain
Domingo Lopez-Rodriguez, University of Malaga, Spain
J.M. Ortiz-de-Lazcano-Lobato, University of Malaga, Spain
In this chapter, two important issues concerning associative memory by neural networks are studied: a
new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns
and performing clustering tasks.
Chapter IX
Artificial Cell Systems Based in Gene Expression Protein Effects 146
Enrique Fernandez-Bianco, University of A Coruna, Spain
Julian Dorado, University of A Coruna, Spain
Nieves Pedreira, University of A Coruna, Spain
This chapter contents a computational model which is inspired in the biologically morphogenesis ideas.
This chapter contains the theoretical development of the model and some simple tests executed over an
implementation of the theoretical model.
Chapter X
Computing vs. Genetics 165
Jose M. Barreiro, Universidad Politecnica de Madrid, Spain
Juan Pazos, Universidad Politecnica de Madrid, Spain
This chapter presents the interrelations between computing and genetics, which both are based on in¬
formation and, particularly, self-reproducing artificial systems.
Chapter XI
Artificial Mind for Virtual Characters 182
lara Moema Oberg Vilela, Universidade Federal do Rio de Janeiro, Brazil
This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an
integrated architecture for an artificial mind that allows various behavior aspects to be simulated in a
coherent and harmonious way, showing believability and computational processing viability.
Chapter XII
A General Rhythmic Pattern Generation Architecture for Legged Locomotion 202
Zhijun Yan, Stirling University, UK
Felipe M.G. Franca, Universidade Federal do Rio de Janeiro, Brazil
This chapter presents a general central pattern generator (CPG) architecture for legged locomotion. Based
on a simple discrete distributed synchronizer, the use of oscillatory building blocks (OBB) is proposed
for the production of complicated rhythmic patterns. An OBB network can be easily built to generate a
full range of locomotion patterns of a legged animal. The modular CPG structure is amenable to very
large scale circuit integration.
Section III
Real-Life Applications with Biologically Inspired Models
Chapter XIII
Genetic Algorithms and Multimodal Search 231
Marcos Gestal, University of A Coruna, Spain
Jose Manuel Vazquez Naya, University of A Coruna, Spain
Norberto Ezquerra, Georgia Institute of Technology, USA
The present chapter tries to establish, the characterisation of the multimodal problems and offers a global
view of some of the several approaches proposed for adapting the classic functioning of the GAs to the
search of multiple solutions. The contributions of the authors and a brief description of several practical
cases of their performance at the real world will be also showed.
Chapter XIV
Bimolecular Computing Devices in Synthetic Biology 250
Jesus M. Miro, Universidad Politecnica de Madrid (UPM), Spain
Alfonso Rodriguez-Paton, Universidad Politecnica de Madrid (UPM), Spain
This chapter focuses on the description of several biomolecular information-processing devices from
both the synthetic biology and biomolecular computation fields. Synthetic biology and biomolecular
computation are disciplines that fuse when it comes to designing and building information processing
devices.
Chapter XV
Guiding Self-Organization in Systems of Cooperative Mobile Agents 268
Alejandro Rodriguez, University of Maryland, USA
Alexander Grushin, University of Maryland, USA
James A. Reggia, University of Maryland, USA
Swarm-intelligence systems involve highly parallel computations across space, based heavily on the
emergence of global behavior through local interactions of components. This chapter describes how
to provide greater control over swarm intelligence systems, and potentially more useful goal-oriented
behavior, by introducing hierarchical controllers in the components
Chapter XVI
Evaluating a Bio-Inspired Approach for the Design of a Grid Information System:
The SO-Grid Portal 291
Agostino Forestiero, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Carlo Mastroianni, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Fausto Pupo, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Giandomenico Spezzano, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
This chapter proposes a bio-inspired approach for the construction of a self-organizing Grid information
system and also describes the SO-Grid Portal, a simulation portal through which registered users can
simulate and analyze ant-based protocols. This chapter can foster the understanding and use of swarm
intelligence, multi-agent and bio-inspired paradigms in the field of distributed computing.
Chapter XVII
Graph Based Evolutionary Algorithms 311
Steven M. Corns, Iowa State University, USA
Daniel A. Ashlock, University of Guelph, Canada
Kenneth Mark Bryden, Iowa State University, USA
This chapter presents Graph Based Evolutionary Algorithms (GBEA). GBEA are a generic enhancement
and diversity»management technique for evolutionary algorithms. GBEA impose restrictions on mating
and placement within the evolving population (new structures are placed in their parents graph neigh¬
borhood). This simulates natural obstacles like geography or social obstacles like the mating dances of
some bird species to mating.
Chapter XVIII
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria 331
Daniela Danciu, University of Craiova, Romania
This chapter aims to explain and analyze the connection between Artificial Intelligence domain require¬
ments and the Theory of Systems with Several Equilibria. This approach allows a better understanding
of those dynamical behaviors of the artificial recurrent neural networks which are desirable for their
proper work i.e. achievement of the tasks they have been designed for.
Chapter XIX
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal
Dispersion Coefficient in Rivers 358
Jianhua Yang, University of Warwick, UK
Evor L. Hines, University of Warwick, UK
IanGuymer, University of Warwick, UK
Daciana D. Iliescu, University of Warwick, UK
Mark S. Leeson, University of Warwick, UK
Gregory P. King, University of Warwick, UK
XuQin Li, University of Warwick, UK
This chapter involves an application of Artificial Intelligence in the field of Civil Engineering, specifi¬
cally the prediction of longitudinal dispersion coefficient in rivers. Based on the concept of Genetic
Algorithms and Artificial Neural Networks, a novel data-driven method called GNMM (Genetic Neural
Mathematical Method) is presented and applied to a very well-studied classic and representative set of
data.
Chapter XX
The Exposition of Fuzzy Decision Trees and Their Application in Biology 375
Malcolm J. Beynon, Cardiff University, UK
Kirsty Park, University of Stirling, UK
This chapter employs the fuzzy decision tree classification technique in a series of biological based
application problems. A small hypothetical example allows the reader to clearly follow the included
analytical rudiments, and the larger applications demonstrate the interpretability allowed through the
use of this approach.
Compilation of References 395
About the Contributors y 422
Index 434
|
| adam_txt |
Table of Contents
Foreword xiii
Preface xv
Section I
Recent Advances in Biological Processes Related to Information Processing
Chapter I
Corticofugal Modulation of Tactile Responses of Neurons in the Spinal Trigetninal Nucleus:
A Wavelet Coherence Study I
Eduardo Malmierca, Universidad Autonoma de Madrid, Spain
Nazareth P. Castellanos, Universidad Complutense de Madrid, Spain
ValeriA. Makarov, Universidad Complutense de Madrid, Spain
Angel Nunez, Universidad Autonoma de Madrid, Spain
Chapter II
Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired
Autonomous Systems 20
Didier Le Ray, Universite de Bordeaux, Lab, MAC, France
Morgane Le Bon-Jego, Universite de Bordeaux, Lab, MAC, France
Daniel Cattaert, Universite de Bordeaux, CNIC, Franc
Chapter III
Forward Dendric Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and
Shifting Output Codes 42
Oscar Herreras, Cajal Institute, Spain
Julia Makarova, Cajal Institute, Spain
Jose Manuel Ibarz, Hospital Ramon y Cajal, Spain
Section II
New Biologically Inspired Artificial Intelligence Models
Chapter IV
Spiking Neural P Systems: An Overview 60
Gheorghe Paun, Institute of Mathematics of the Romanian Academy, Romania
Mario J. Perez-Jimenez, University ofSevilla, Spain
Chapter V
Simulation of the Action Potential in the Neuron's Membrane in Artificial Neural Networks 74
Juan Ramon Rabunal Dopico, University ofCoruna, Spain
Javier Pereira Loureiro, University ofCoruna, Spain
Monica Miguelez Rico, University ofCoruna, Spain
Chapter VI
Recent Methodology in Connectionist Systems 94
Ana B. Porto Pazos, University of A Coruna, Spain
Alberto Alvarellos Gonzalez, University of A Coruna, Spain
Alejandro Pazos Sierra, University of A Coruna, Spain
Chapter VII
A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in
Evolutionary Robotics 107
Jose A. Fernandez-Leon, University of Sussex, UK CONICET, Argentina
Gerardo G. Acosta, Univ. Nac. del Centro de la Prov. de Buenos Aires CONICET,
Argentina
Miguel A. Mayosky, Univ. Nac. de la Plata CICPBA, Argentina
Oscar C. Ibdnez, Universitat de les Illes Balears, Palma de Mallorca, Spain
Chapter VIII
An Approach to Artificial Concept Learning Based on Human Concept Learning by
Using Artificial Neural Networks 130
Enrique Merida-Casermeiro, University of Malaga, Spain
Domingo Lopez-Rodriguez, University of Malaga, Spain
J.M. Ortiz-de-Lazcano-Lobato, University of Malaga, Spain
Chapter IX
Artificial Cell Systems Based in Gene Expression Protein Effects 146
Enrique Fernandez-Bianco, University of A Coruna, Spain
Julian Dorado, University of A Coruna, Spain
Nieves Pedreira, University of A Coruna, Spain
Chapter X
Computing vs. Genetics 165
Jose M. Barreiro, Universidad Politecnica de Madrid, Spain
Juan Pazos, Universidad Politecnica de Madrid, Spain
Chapter XI
Artificial Mind for Virtual Characters 182
lara Moema Oberg Vilela, Universidade Federal do Rio de Janeiro, Brazil
Chapter XII
A General Rhythmic Pattern Generation Architecture for Legged Locomotion 202
Zhijun Yan, Stirling University, UK
Felipe M.G. Franca, Universidade Federal do Rio de Janeiro, Brazil
Section III
Real-Life Applications with Biologically Inspired Models
Chapter XIII
Genetic Algorithms and Multimodal Search 231
Marcos Gestal, University of A Coruna, Spain
Jose Manuel Vazquez Naya, University of A Coruna, Spain
Norberto Ezquerra, Georgia Institute of Technology, USA
Chapter XIV
Bimolecular Computing Devices in Synthetic Biology 250
Jesus M. Miro, Universidad Politecnica de Madrid (UPM), Spain
Alfonso Rodriguez-Paton, Universidad Politecnica de Madrid (UPM), Spain
Chapter XV
Guiding Self-Organization in Systems of Cooperative Mobile Agents 268
Alejandro Rodriguez, University of Maryland, USA
Alexander Grushin, University of Maryland, USA
James A. Reggia, University of Maryland, USA
Chapter XVI
Evaluating a Bio-Inspired Approach for the Design of a Grid Information System:
The SO-Grid Portal 291
Agostino Forestiero, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Carlo Mastroianni, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Fausto Pupo, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Giandomenico Spezzano, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Chapter XVII
Graph Based Evolutionary Algorithms 311
Steven M. Corns, Iowa State University, USA
Daniel A. Ashlock, University ofGuelph, Canada
Kenneth Mark Bryden, Iowa State University, USA
Chapter XVIII
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria 331
Daniela Danciu, University of Craiova, Romania
Chapter XIX
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal
Dispersion Coefficient in Rivers 358
Jianhua Yang, University of Warwick, UK
Evor L. Hines, University of Warwick, UK
lanGuymer, University of Warwick, UK
Daciana D. Iliescu, University of Warwick, UK
MarkS. Leeson, University of Warwick, UK
Gregory P. King, University of Warwick, UK
XuQin Li, University of Warwick, UK
Chapter XX
The Exposition of Fuzzy Decision Trees and Their Application in Biology 375
Malcolm J. Beynon, Cardiff University, UK
Kir sty Park, University of Stirling, UK
Compilation of References 395
About the Contributors 422
Index 434
Detailed Table of Contents
Foreword xiii
Preface xv
Section 1
Recent Advances in Biological Processes Related to Information Processing
Chapter I
Corticofugal Modulation of Tactile Responses of Neurons in the Spinal Trigeminal Nucleus:
A Wavelet Coherence Study I
Eduardo Malmierca, Universidad Autonoma de Madrid, Spain
Nazareth P. Castellanos, Universidad Compluteme de Madrid, Spain
Valeri A. Makarov, Universidad Complutense de Madrid, Spain
Angel Nunez, Universidad Autonoma de Madrid, Spain
Recent electrophysiological studies indicate the existence of an important somatosensory processing in
the trigeminal nucleus which is modulated by the corticofugal projection from the somatosensory cortex.
This chapter studies a new mathematical analysis of the temporal structure of neuronal responses during
tactile stimulation of the spinal trigeminal nucleus.
Chapter II
Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired
Autonomous Systems 20
Didier Le Ray, Universite de Bordeaux, Lab, MAC, France
Morgane Le Bon-Jego, Universite de Bordeaux, Lab, MAC, France
Daniel Cattaert, Universite de Bordeaux, CN1C, Franc
Knowledge in invertebrate neuroethology has demonstrated unique advantages for engineering bio¬
logically-based autonomous systems. This chapter aims at presenting some basic neuronal mechanisms
involved in crayfish walking and postural control involving a single key joint of the leg. Due to its rela¬
tive simplicity, the neuronal network responsible for these motor functions is a suitable model for under¬
standing how sensory and motor components interact in the elaboration of appropriate movement and,
therefore, for providing basic principles essential to the design of autonomous embodied systems.
Chapter III
Forward Dendric Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and
Shifting Output Codes 42
Oscar Herreras, Cajal Institute, Spain
Julia Makarova, Cajal Institute, Spain
Jose Manuel Ibarz, Hospital Ramon y Cajal, Spain
This chapter reviews the underlying mechanisms and theoretical implications of the role of voltage-de¬
pendent dendritic currents on the forward transmission of synaptic inputs. The notion analysed brakes
with the classic view of neurons as the elementary units of the brain and attributes them computational/
storage capabilities earlier billed to complex brain circuits.
Section II
New Biologically Inspired Artificial Intelligence Models
Chapter IV
Spiking Neural P Systems: An Overview 60
Gheorghe Paun, Institute of Mathematics of the Romanian Academy, Romania
Mario J. Perez-Jimenez, University ofSevilla, Spain
This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was
recently introduced with motivation from neural computing based on spiking.
Chapter V
Simulation of the Action Potential in the Neuron's Membrane in Artificial Neural Networks 74
Juan Ramon Rabuhal Dopico, University ofCoruna, Spain
Javier Pereira Loureiro, University ofCoruna, Spain
Monica Miguelez Rico, University ofCoruna, Spain
This chapter presents an evolution of the Recurrent ANN (RANN) to enforce the persistence of activa¬
tions within the neurons to create activation contexts that generate correct outputs through time. The
aim of this work is to develop a process element model with activation output much more similar to the
biological neurons one.
Chapter VI
Recent Methodology in Connectionist Systems 94
Ana B. Porto Pazos, University of A Coruna, Spain
Alberto Alvarellos Gonzalez, University of A Coruna, Spain
Alejandro Pazos Sierra, University of A Coruna, Spain
This chapter presents an evolution of the Recurrent ANN (RANN) to enforce the persistence of activa¬
tions within the neurons to create activation contexts that generate correct outputs through time. The
aim of this work is to develop a process element model with activation output much more similar to the
biological neurons one.
Chapter VII
A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in
Evolutionary Robotics 107
Jose A. Fernandez-Leon, University of Sussex, UK CONICET, Argentina
Gerardo G. Acosta, Univ. Nac. del Centro de la Prov. de Buenos Aires CONICET,
Argentina
Miguel A. Mayosky, Univ. Nac. de la Plata CICPBA, Argentina
Oscar C. Ibdnez, Universitat de les Illes Balears, Palma de Mallorca, Spain
This chapter presents the experience gained when developing the path generation modules of autono¬
mous robots, starting with traditional artificial intelligence approaches and ending with the most recent
techniques of Evolutionary Robotics. Discussions around the features and suitability of each technique,
with special interest on immune-based behaviour coordination are proposed to meet the corresponding
theoretical arguments supported by empirical experiences.
Chapter VIII
An Approach to Artificial Concept Learning Based on Human Concept Learning by
Using Artificial Neural Networks 130
Enrique Merida-Casermeiro, University of Malaga, Spain
Domingo Lopez-Rodriguez, University of Malaga, Spain
J.M. Ortiz-de-Lazcano-Lobato, University of Malaga, Spain
In this chapter, two important issues concerning associative memory by neural networks are studied: a
new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns
and performing clustering tasks.
Chapter IX
Artificial Cell Systems Based in Gene Expression Protein Effects 146
Enrique Fernandez-Bianco, University of A Coruna, Spain
Julian Dorado, University of A Coruna, Spain
Nieves Pedreira, University of A Coruna, Spain
This chapter contents a computational model which is inspired in the biologically morphogenesis ideas.
This chapter contains the theoretical development of the model and some simple tests executed over an
implementation of the theoretical model.
Chapter X
Computing vs. Genetics 165
Jose M. Barreiro, Universidad Politecnica de Madrid, Spain
Juan Pazos, Universidad Politecnica de Madrid, Spain
This chapter presents the interrelations between computing and genetics, which both are based on in¬
formation and, particularly, self-reproducing artificial systems.
Chapter XI
Artificial Mind for Virtual Characters 182
lara Moema Oberg Vilela, Universidade Federal do Rio de Janeiro, Brazil
This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an
integrated architecture for an artificial mind that allows various behavior aspects to be simulated in a
coherent and harmonious way, showing believability and computational processing viability.
Chapter XII
A General Rhythmic Pattern Generation Architecture for Legged Locomotion 202
Zhijun Yan, Stirling University, UK
Felipe M.G. Franca, Universidade Federal do Rio de Janeiro, Brazil
This chapter presents a general central pattern generator (CPG) architecture for legged locomotion. Based
on a simple discrete distributed synchronizer, the use of oscillatory building blocks (OBB) is proposed
for the production of complicated rhythmic patterns. An OBB network can be easily built to generate a
full range of locomotion patterns of a legged animal. The modular CPG structure is amenable to very
large scale circuit integration.
Section III
Real-Life Applications with Biologically Inspired Models
Chapter XIII
Genetic Algorithms and Multimodal Search 231
Marcos Gestal, University of A Coruna, Spain
Jose Manuel Vazquez Naya, University of A Coruna, Spain
Norberto Ezquerra, Georgia Institute of Technology, USA
The present chapter tries to establish, the characterisation of the multimodal problems and offers a global
view of some of the several approaches proposed for adapting the classic functioning of the GAs to the
search of multiple solutions. The contributions of the authors and a brief description of several practical
cases of their performance at the real world will be also showed.
Chapter XIV
Bimolecular Computing Devices in Synthetic Biology 250
Jesus M. Miro, Universidad Politecnica de Madrid (UPM), Spain
Alfonso Rodriguez-Paton, Universidad Politecnica de Madrid (UPM), Spain
This chapter focuses on the description of several biomolecular information-processing devices from
both the synthetic biology and biomolecular computation fields. Synthetic biology and biomolecular
computation are disciplines that fuse when it comes to designing and building information processing
devices.
Chapter XV
Guiding Self-Organization in Systems of Cooperative Mobile Agents 268
Alejandro Rodriguez, University of Maryland, USA
Alexander Grushin, University of Maryland, USA
James A. Reggia, University of Maryland, USA
Swarm-intelligence systems involve highly parallel computations across space, based heavily on the
emergence of global behavior through local interactions of components. This chapter describes how
to provide greater control over swarm intelligence systems, and potentially more useful goal-oriented
behavior, by introducing hierarchical controllers in the components
Chapter XVI
Evaluating a Bio-Inspired Approach for the Design of a Grid Information System:
The SO-Grid Portal 291
Agostino Forestiero, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Carlo Mastroianni, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Fausto Pupo, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
Giandomenico Spezzano, Institute of High Performance Computing and Networking
CNR-ICAR, Italy
This chapter proposes a bio-inspired approach for the construction of a self-organizing Grid information
system and also describes the SO-Grid Portal, a simulation portal through which registered users can
simulate and analyze ant-based protocols. This chapter can foster the understanding and use of swarm
intelligence, multi-agent and bio-inspired paradigms in the field of distributed computing.
Chapter XVII
Graph Based Evolutionary Algorithms 311
Steven M. Corns, Iowa State University, USA
Daniel A. Ashlock, University of Guelph, Canada
Kenneth Mark Bryden, Iowa State University, USA
This chapter presents Graph Based Evolutionary Algorithms (GBEA). GBEA are a generic enhancement
and diversity»management technique for evolutionary algorithms. GBEA impose restrictions on mating
and placement within the evolving population (new structures are placed in their parents' graph neigh¬
borhood). This simulates natural obstacles like geography or social obstacles like the mating dances of
some bird species to mating.
Chapter XVIII
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria 331
Daniela Danciu, University of Craiova, Romania
This chapter aims to explain and analyze the connection between Artificial Intelligence domain require¬
ments and the Theory of Systems with Several Equilibria. This approach allows a better understanding
of those dynamical behaviors of the artificial recurrent neural networks which are desirable for their
proper work i.e. achievement of the tasks they have been designed for.
Chapter XIX
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal
Dispersion Coefficient in Rivers 358
Jianhua Yang, University of Warwick, UK
Evor L. Hines, University of Warwick, UK
IanGuymer, University of Warwick, UK
Daciana D. Iliescu, University of Warwick, UK
Mark S. Leeson, University of Warwick, UK
Gregory P. King, University of Warwick, UK
XuQin Li, University of Warwick, UK
This chapter involves an application of Artificial Intelligence in the field of Civil Engineering, specifi¬
cally the prediction of longitudinal dispersion coefficient in rivers. Based on the concept of Genetic
Algorithms and Artificial Neural Networks, a novel data-driven method called GNMM (Genetic Neural
Mathematical Method) is presented and applied to a very well-studied classic and representative set of
data.
Chapter XX
The Exposition of Fuzzy Decision Trees and Their Application in Biology 375
Malcolm J. Beynon, Cardiff University, UK
Kirsty Park, University of Stirling, UK
This chapter employs the fuzzy decision tree classification technique in a series of biological based
application problems. A small hypothetical example allows the reader to clearly follow the included
analytical rudiments, and the larger applications demonstrate the interpretability allowed through the
use of this approach.
Compilation of References 395
About the Contributors y 422
Index 434 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
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| dewey-sort | 3610.285 263 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| discipline_str_mv | Medizin |
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| spelling | Advancing artificial intelligence through biological process applications Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta, [eds.] Hershey, PA Medical Information Science Reference 2009 XXII, 436 S. Ill. 29 cm txt rdacontent n rdamedia nc rdacarrier "Premier reference source"--Cover Includes bibliographical references (p. 395-421) and index "This book presents recent advancements in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, it will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres"--Provided by publisher Artificial intelligence / Medical applications Artificial intelligence / Biological applications Neural networks (Neurobiology) Künstliche Intelligenz Artificial Intelligence Artificial intelligence Biological applications Artificial intelligence Medical applications Medical Informatics Applications Models, Neurological Neural Networks (Computer) Bioinformatik (DE-588)4611085-9 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Bioinformatik (DE-588)4611085-9 s DE-604 Pazos, Ana B. Porto Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016699642&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advancing artificial intelligence through biological process applications Artificial intelligence / Medical applications Artificial intelligence / Biological applications Neural networks (Neurobiology) Künstliche Intelligenz Artificial Intelligence Artificial intelligence Biological applications Artificial intelligence Medical applications Medical Informatics Applications Models, Neurological Neural Networks (Computer) Bioinformatik (DE-588)4611085-9 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4033447-8 (DE-588)4143413-4 |
| title | Advancing artificial intelligence through biological process applications |
| title_auth | Advancing artificial intelligence through biological process applications |
| title_exact_search | Advancing artificial intelligence through biological process applications |
| title_exact_search_txtP | Advancing artificial intelligence through biological process applications |
| title_full | Advancing artificial intelligence through biological process applications Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta, [eds.] |
| title_fullStr | Advancing artificial intelligence through biological process applications Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta, [eds.] |
| title_full_unstemmed | Advancing artificial intelligence through biological process applications Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta, [eds.] |
| title_short | Advancing artificial intelligence through biological process applications |
| title_sort | advancing artificial intelligence through biological process applications |
| topic | Artificial intelligence / Medical applications Artificial intelligence / Biological applications Neural networks (Neurobiology) Künstliche Intelligenz Artificial Intelligence Artificial intelligence Biological applications Artificial intelligence Medical applications Medical Informatics Applications Models, Neurological Neural Networks (Computer) Bioinformatik (DE-588)4611085-9 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Artificial intelligence / Medical applications Artificial intelligence / Biological applications Neural networks (Neurobiology) Künstliche Intelligenz Artificial Intelligence Artificial intelligence Biological applications Artificial intelligence Medical applications Medical Informatics Applications Models, Neurological Neural Networks (Computer) Bioinformatik Aufsatzsammlung |
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