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Retinal Development
This advanced text, first published in 2006, takes a developmental approach to the presentation of our understanding of how vertebrates construct a retina. Written by experts in the field, each of the seventeen chapters covers a specific step in the process, focusing on the underlying molecular, cellular, and physiological mechanisms. There is also a special section on emerging technologies, including genomics, zebrafish genetics, and stem cell biology that are starting to yield important insights into retinal development. Primarily aimed at professionals, both biologists and clinicians working with the retina, this book provides a concise view of vertebrate retinal development. Since the retina is 'an approachable part of the brain', this book will also be attractive to all neuroscientists interested in development, as processes required to build this exquisitely organized system are ultimately relevant to all other parts of the central nervous system.
Modeling Neural Development
An important collection showing how computational and mathematical modeling can be used to study the complexities of neural development.
Quantitative analysis of neuroanatomy
The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. Large-scale projects were recently launched with the aim of providing infrastructure for brain simulations. These projects will increase the need for a precise understanding of brain structure, e.g., through statistical analysis and models. From articles in this Research Topic, we identify three main themes that clearly illustrate how new quantitative approaches are helping advance our understanding of neural structure and function. First, new approaches to reconstruct neurons and circuits from empirical dat...
Vision and Brain
An engaging introduction to the science of vision that offers a coherent account of vision based on general information processing principles In this accessible and engaging introduction to modern vision science, James Stone uses visual illusions to explore how the brain sees the world. Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. Stone draws together results from David Marr's computational framework, Barlow's efficient coding hypothesis, Bayesian inference, Shannon's information theory, and signal processing to construct a coherent account of vision that explai...
Enchanted America
America is in civic chaos, its politics rife with conspiracy theories and false information. Nationalism and authoritarianism are on the rise, while scientists, universities, and news organizations are viewed with increasing mistrust. Its citizens reject scientific evidence on climate change and vaccinations while embracing myths of impending apocalypse. And then there is Donald Trump, a presidential candidate who won the support of millions of conservative Christians despite having no moral or political convictions. What is going on? The answer, according to J. Eric Oliver and Thomas J. Wood, can be found in the most important force shaping American politics today: human intuition. Much of ...
In Vitro Neuronal Networks
This book provides a comprehensive overview of the incredible advances achieved in the study of in vitro neuronal networks for use in basic and applied research. These cultures of dissociated neurons offer a perfect trade-off between complex experimental models and theoretical modeling approaches giving new opportunities for experimental design but also providing new challenges in data management and interpretation. Topics include culturing methodologies, neuroengineering techniques, stem cell derived neuronal networks, techniques for measuring network activity, and recent improvements in large-scale data analysis. The book ends with a series of case studies examining potential applications of these technologies.
Wavelets and Statistics
Despite its short history, wavelet theory has found applications in a remarkable diversity of disciplines: mathematics, physics, numerical analysis, signal processing, probability theory and statistics. The abundance of intriguing and useful features enjoyed by wavelet and wavelet packed transforms has led to their application to a wide range of statistical and signal processing problems. On November 16-18, 1994, a conference on Wavelets and Statistics was held at Villard de Lans, France, organized by the Institute IMAG-LMC, Grenoble, France. The meeting was the 15th in the series of the Rencontres Pranco-Belges des 8tatisticiens and was attended by 74 mathematicians from 12 different countr...
Principles of Computational Modelling in Neuroscience
Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.
Independent Component Analysis
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.
Information Theory
Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.
