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Computational Neurogenetic Modeling by Lubica Benuskova,Nikola K. Kasabov Pdf
This is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines.
Computational Neurogenetic Modeling by Lubica Benuskova,Nikola Kasabov Pdf
This is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines.
What Is Bio Inspired Computing The subject of research known as bio-inspired computing, which is short for biologically inspired computing, is one that focuses on finding solutions to issues in computer science by employing biological models. Connectionism, emergent behavior, and emergence are all related to this concept. Within the field of computer science, artificial intelligence and machine learning are related to the concept of bio-inspired computing. The field of natural computation includes a significant subfield known as bio-inspired computing. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bio-inspired computing Chapter 2: Connectionism Chapter 3: Evolutionary computation Chapter 4: Computational neuroscience Chapter 5: Neuromorphic engineering Chapter 6: Cognitive architecture Chapter 7: Neural network Chapter 8: Artificial brain Chapter 9: Computational neurogenetic modeling Chapter 10: Spiking neural network (II) Answering the public top questions about bio inspired computing. (III) Real world examples for the usage of bio inspired computing in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bio inspired computing. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Computational Intelligence Methods for Bioinformatics and Biostatistics by Elia Biganzoli,Alfredo Vellido,Federico Ambrogi,Roberto Tagliaferri Pdf
This book constitutes the thoroughly refereed post-proceedings of the 8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2011, held in Gargnano del Garda, Italy, in June/July 2011. The 19 papers, presented together with 2 keynote speeches, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on statistical learning, genomics, computational intelligence for health at the edge, proteomics, intelligent clinical decision support systems (i-CDSS), bioinformatics, and data clustering.
Computational Modeling of Gene Regulatory Networks by Hamid Bolouri Pdf
This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.
Computational Modeling and Simulation of Intellect: Current State and Future Perspectives by Igelnik, Boris Pdf
"This book confronts the problem of meaning by fusing together methods specific to different fields and exploring the computational efficiency and scalability of these methods"--Provided by publisher.
Computational Intelligence in Medical Informatics by Arpad Kelemen,Ajith Abraham,Yulan Liang Pdf
Medical Informatics (MI) is an emerging interdisciplinary science. This book deals with the application of computational intelligence in MI. Addressing the various issues of medical informatics using different computational intelligence approaches is the novelty of this edited volume. This volume comprises of 15 chapters selected on the basis of fundamental ideas/concepts including an introductory chapter giving the fundamental definitions and some important research challenges.
Advances in Computational Intelligence by Jing Liu,Cesare Alippi,Bernadette Bouchon-Meunier,Garrison W. Greenwood,Hussein A. Abbass Pdf
This state-of-the-art survey offers a renewed and refreshing focus on the progress in evolutionary computation, in neural networks, and in fuzzy systems. The book presents the expertise and experiences of leading researchers spanning a diverse spectrum of computational intelligence in these areas. The result is a balanced contribution to the research area of computational intelligence that should serve the community not only as a survey and a reference, but also as an inspiration for the future advancement of the state of the art of the field. The 13 selected chapters originate from lectures and presentations given at the IEEE World Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Challenges for Computational Intelligence by Jacek Mandziuk Pdf
In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.
Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 by Wlodzislaw Duch,Erkki Oja,Slawomir Zadrozny Pdf
This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.
What Is Neuromorphic Engineering Neuromorphic computing and neuromorphic engineering are both terms that refer to the same thing: the use of very-large-scale integration (VLSI) systems that incorporate electrical analog circuits to simulate neuro-biological structures that are found in the nervous system. Any electronic device that does calculations with the help of artificial neurons that are implemented as physical structures is referred to as a neuromorphic computer or chip. Recently, the word "neuromorphic" has been used to refer to analog, digital, mixed-mode analog/digital VLSI, and software systems that embody models of brain systems. This use of the term has become more common. To actualize the implementation of neuromorphic computing on the hardware level, oxide-based memristors, spintronic memory, threshold switches, and transistors are some of the components that may be used. Training software-based neuromorphic systems of spiking neural networks can be accomplished through the use of error backpropagation, for instance through the utilization of Python-based frameworks like snnTorch, or through the utilization of canonical learning rules from the biological learning literature, for instance through the utilization of BindsNet. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuromorphic engineering Chapter 2: Artificial neuron Chapter 3: Bio-inspired computing Chapter 4: Steve Furber Chapter 5: Carver Mead Chapter 6: Recurrent neural network Chapter 7: Neural network Chapter 8: Wetware computer Chapter 9: Computational neurogenetic modeling Chapter 10: Spiking neural network Chapter 11: Neurorobotics Chapter 12: Misha Mahowald Chapter 13: Memristor Chapter 14: Physical neural network Chapter 15: NOMFET Chapter 16: Massimiliano Versace Chapter 17: Kwabena Boahen Chapter 18: SpiNNaker Chapter 19: Cognitive computer Chapter 20: Glossary of artificial intelligence Chapter 21: Hai Li (II) Answering the public top questions about neuromorphic engineering. (III) Real world examples for the usage of neuromorphic engineering in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neuromorphic engineering' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuromorphic engineering.
Computational Intelligence in Biomedicine and Bioinformatics by Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-Ella Hassanien Pdf
The purpose of this book is to provide an overview of state-of-the-art methodologies currently utilized for biomedicine and/or bioinformatics-oriented applications. Researchers working in these fields will learn new methods to help tackle their problems.
Computational Intelligence, Theory and Applications by Bernd Reusch Pdf
This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, Dortmund, Germany, 2006. This conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. The papers presented here, all thoroughly reviewed, are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.
Handbook On Computational Intelligence (In 2 Volumes) by Angelov Plamen Parvanov Pdf
With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of 'digital obesity'. In this century, computational intelligence, such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas — from social sciences, economics and biology, medicine and social networks, to cyber security.The Handbook of Computational Intelligence (in two volumes) prompts readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems. Volume one explores the subjects of fuzzy logic and systems, artificial neural networks, and learning systems. Volume two delves into evolutionary computation, hybrid systems, as well as the applications of computational intelligence in decision making, the process industry, robotics, and autonomous systems.This work is a 'one-stop-shop' for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence by Nikola K. Kasabov Pdf
Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.