Author: Maria Quincy Ferdinand
Release Date: 2013-01
Genre: Social Science
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Peter Dayan is the director of the Gatsby Computational Neuroscience Unit at the University College London. He is the a co-author of 'Theoretical Neuroscience', a leading textbook of computational and mathematical modeling of neural systems (see computational neuroscience). He is known for applying Bayesian methods from machine learning and Artificial Intelligence as theories of neural function, especially relating neurotransmitter levels to Bayesian uncertainties. He began his career studying Mathematics at Cambridge followed by a PhD in Artificial Intelligence at Edinburgh, which focused on reinforcement learning. He then went on to do a Postdoc at the Massachusetts Institute of Technology in Boston.
Author: Erik De Schutter
Release Date: 2004
The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.
Author: Giovanna Citti
Publisher: Springer Science & Business Media
Release Date: 2014-02-08
This book is devoted to the study of the functional architecture of the visual cortex. Its geometrical structure is the differential geometry of the connectivity between neural cells. This connectivity is building and shaping the hidden brain structures underlying visual perception. The story of the problem runs over the last 30 years, since the discovery of Hubel and Wiesel of the modular structure of the primary visual cortex, and slowly cams towards a theoretical understanding of the experimental data on what we now know as functional architecture of the primary visual cortex. Experimental data comes from several domains: neurophysiology, phenomenology of perception and neurocognitive imaging. Imaging techniques like functional MRI and diffusion tensor MRI allow to deepen the study of cortical structures. Due to this variety of experimental data, neuromathematematics deals with modelling both cortical structures and perceptual spaces. From the mathematical point of view, neuromathematical call for new instruments of pure mathematics: sub-Riemannian geometry models horizontal connectivity, harmonic analysis in non commutative groups allows to understand pinwheels structure, as well as non-linear dimensionality reduction is at the base of many neural morphologies and possibly of the emergence of perceptual units. But at the center of the neurogeometry is the problem of harmonizing contemporary mathematical instruments with neurophysiological findings and phenomenological experiments in an unitary science of vision. The contributions to this book come from the very founders of the discipline.
Author: Wanpracha Chaovalitwongse
Publisher: Springer Science & Business Media
Release Date: 2010-07-03
This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.
Das Jahr 2045 markiert einen historischen Meilenstein: Es ist das Jahr, in dem der Mensch seine biologischen Begrenzungen mithilfe der Technik überwinden wird. Diese als technologische Singularität bekannt gewordene Revolution wird die Menschheit für immer verändern. Googles Chefingenieur Ray Kurzweil, dessen wahnwitzigen Visionen in den vergangenen Jahrzehnten immer wieder genau ins Schwarze trafen, zeichnet in diesem Klassiker des Transhumanismus mit beispielloser Detailwut eine bunt schillernde Momentaufnahme der technischen Evolution und legt dar, weshalb diese so bald kein Ende finden, sondern im Gegenteil immer weiter an Dynamik gewinnen wird. Daraus ergibt sich eine ebenso faszinierende wie schockierende Vision für die Zukunft der Menschheit.
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--
How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding. Computational Neuroscience: A Comprehensive Approach provides a unified treatment of the mathematical theory of the nervous system and presents concrete examples demonstrating how computational techniques can illuminate difficult neuroscience problems. In chapters contributed by top researchers, the book introduces the basic mathematical concepts, then examines modeling at all levels, from single-channel and single neuron modeling to neuronal networks and system-level modeling. The emphasis is on models with close ties to experimental observations and data, and the authors review application of the models to systems such as olfactory bulbs, fly vision, and sensorymotor systems. Understanding the nature and limits of the strategies neural systems employ to process and transmit sensory information stands among the most exciting and difficult challenges faced by modern science. This book clearly shows how computational neuroscience has and will continue to help meet that challenge.
Author: Claudius Gros
Publisher: Springer Science & Business Media
Release Date: 2008-01-01
Helping us understand our complex world, this book presents key findings in quantitative complex system science. Its approach is modular and phenomenology driven. Examples of phenomena treated in the book include the small world phenomenon in social and scale-free networks; life at the edge of chaos; the concept of living dynamical systems; and emotional diffusive control within cognitive system theory. Each chapter includes exercises to test your grasp of new material. Written at an introductory level, the author provides an accessible entry for graduate students in physics, mathematics, and theoretical computer science.
Author: G. Bard Ermentrout
Publisher: Springer Science & Business Media
Release Date: 2010-07-01
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
Author: Partha Mitra
Publisher: Oxford University Press
Release Date: 2007-12-07
The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area. The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It may also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing. The first part of the book contains a set of essays meant to provide conceptual background which are not technical and shall be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics.
The lack of scientists equally trained and prepared to understand both mathematics and biology/medicine hampers the development and application of computer simulation methods in biology and neurogastrobiology. Currently, there are no texts for navigating the extensive and intricate field of mathematical and computational modeling in neurogastrobiology. This book bridges the gap between mathematicians, computer scientists and biologists, and thus assists in the study and analysis of complex biological phenomena that cannot be done through traditional in vivo and in vitro experimental approaches.The book recognizes the complexity of biological phenomena under investigation and treats the subject matter with a degree of mathematical rigor. Special attention is given to computer simulations for interpolation and extrapolation of electromechanical and chemoelectrical phenomena, nonlinear self-sustained electromechanical wave activity, pharmacological effects including co-localization and co-transmission by multiple neurotransmitters, receptor polymodality, and drug interactions.Mathematical Modeling and Simulation in Enteric Neurobiology is an interdisciplinary book and is an essential source of information for biologists and doctors who are interested in knowing about the role and advantages of numerical experimentation in their subjects, as well as for mathematicians who are interested in exploring new areas of applications.
Author: P. Wang
Publisher: IOS Press
Release Date: 2008-02-18
The field of Artificial Intelligence (AI) was initially directly aimed at the construction of ‘thinking machines’ – that is, computer systems with human-like general intelligence. But this task proved more difficult than expected. As the years passed, AI researchers gradually shifted focus to producing AI systems that intelligently approached specific tasks in relatively narrow domains. In recent years, however, more and more AI researchers have recognized the necessity – and the feasibility – of returning to the original goal of the field. Increasingly, there is a call to focus less on highly specialized ‘narrow AI’ problem solving systems, and more on confronting the difficult issues involved in creating ‘human-level intelligence’, and ultimately general intelligence that goes beyond the human level in various ways. Artificial General Intelligence (AGI), as this renewed focus has come to be called, attempts to study and reproduce intelligence as a whole in a domain independent way. Encouraged by the recent success of several smaller-scale AGI-related meetings and special tracks at conferences, the initiative to organize the very first international conference on AGI was taken, with the goal to give researchers in the field an opportunity to present relevant research results and to exchange ideas on topics of common interest. In this collection you will find the conference papers: full-length papers, short position statements and also the papers presented in the post conference workshop on the sociocultural, ethical and futurological implications of AGI.