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Models of Neural Networks
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the ...
50 Years Of The Renormalization Group: Dedicated To The Memory Of Michael E Fisher
The contributions in the book are devoted to the memory of Michael E Fisher, and hence include many personal memories from people whose work was influenced by him. Also, the book is a collection of articles from leaders in the field of phase transitions and critical phenomena, to celebrate 50 years of the renormalization group and the 1972 paper by Wilson and Fisher. Many of the articles review, in tutorial form, the progress in the fields of phase transitions and the renormalization group.
Transactions on Computational Systems Biology IX
The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems oriented point of view of biological phenomena. This issue contains four highly detailed papers. The first paper focuses on quantitative aspects of the bgl operon for E.coli. The second contribution deals with ecosystem transitions affecting phenotype expressions and selection mechanisms. The third paper presents the Stochastic Calculus of Looping Sequences (SCLS) suitable for the description of microbiological systems, such as cellular pathways, and their evolution. The final contribution describes the use of biological transactions to make atomic sequences of interactions in the BlenX language.
Advances in Research and Applications
Under the capable and qualified editorial leadership of Dr. Gerald Litwack, Vitamins and Hormones continues to publish cutting-edge reviews of interest to endocrinologists, biochemists, nutritionists, pharmacologists, cell biologists, and molecular biologists. First published in 1943, Vitamins and Hormones is the longest-running serial published by Academic Press. In the early days of the Serial, the subjects of vitamins and hormones were quite distinct. Now, new discoveries have proved that several of the vitamins function as hormones and many of the substances inferred by the title of the serial function in signal transduction processes. Accordingly, the Editor-in-Chief has expanded the scope of the serial to reflect this newer understanding of function-structure relationships in cellular communication. The Editorial Board now reflects expertise in the field of hormone action, vitamin action, X-ray crystal structure, physiology, and enzyme mechanisms.
Transactions on Computational Systems Biology VII
This volume, the 7th in the Transactions on Computational Systems Biology series, contains a fully refereed and carefully selected set of papers from two workshops: BioConcur 2004 held in London, UK in August 2004 and BioConcur 2005 held in San Francisco, CA, USA in August 2005. The 8 papers chosen for this special issue are devoted to various aspects of computational methods, algorithms, and techniques in bioinformatics.
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mi...
Quantitative Viral Ecology
When we think about viruses we tend to consider ones that afflict humans—such as those that cause influenza, HIV, and Ebola. Yet, vastly more viruses infect single-celled microbes. Diverse and abundant, microbes and the viruses that infect them are found in oceans, lakes, plants, soil, and animal-associated microbiomes. Taking a vital look at the "microscopic" mode of disease dynamics, Quantitative Viral Ecology establishes a theoretical foundation from which to model and predict the ecological and evolutionary dynamics that result from the interaction between viruses and their microbial hosts. Joshua Weitz addresses three major questions: What are viruses of microbes and what do they do t...
