Neuroscience Databases

Neuroscience Databases

Author: Rolf Kötter

Publisher: Springer Science & Business Media

Published: 2003

Total Pages: 340

ISBN-13: 9781402071652

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Neuroscience Databases: A Practical Guide is the first book providing a comprehensive overview of these increasingly important databases. This volume makes the results of the Human Genome Project and other recent large-scale initiatives in the neurosciences available to a wider community. It extends the scope of bioinformatics from the molecular to the cellular, microcircuitry and systems levels, dealing for the first time with complex neuroscientific issues and leading the way to a new culture of data sharing and data mining necessary to successfully tackle neuroscience questions. Aimed at the novice user who wants to access the data, it provides clear and concise instructions on how to download the available data sets and how to use the software with a minimum of technical detail with most chapters written by the database creators themselves. Key databases and topics include: -Neuroinformatics for C. Elegans; -Gene Expression Patterns; -Functional Analyses of Olfactory Receptors -Protein-Protein Interactions; -Web-Based Neuronal Archives; -Neuronal and Network Modeling; -Storage and Retrieval of Experimental Data for Biophysically Realistic Modeling; -Analysis of Spike Trains; -Neural Connectivity Patterns; -Software Tools for Neuroimaging; -Data Management, Inspection and Sharing.


Databasing the Brain

Databasing the Brain

Author: Steven H. Koslow

Publisher:

Published: 2005-03-10

Total Pages: 488

ISBN-13:

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Covers both basic principles and specific applications across a range of problems in brain research. It truly integrates neuroscience with informatics, providing a means for understanding the new analytical tools and models of neuronal functions now being developed. Each chapter offers practical guidance for applying this knowledge to current research, enhancing electronic collaborations, and formulating hypotheses.


Neuroinformatics for Neuropsychology

Neuroinformatics for Neuropsychology

Author: Vinoth Jagaroo

Publisher: Springer Science & Business Media

Published: 2009-08-11

Total Pages: 133

ISBN-13: 1441900608

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Bioinformatics involves specialized application of computer technology to investigative and conceptual problems in biology and medicine; neuroinformatics (NI) is the practice of bioinformatics in the neurosciences. Over the past two decades the biomedical sciences have been revolutionized by databases, data mining and data modeling techniques. The Human Genome Project, which depended on informatics methods, has been the most well recognized bioinformatics undertaking. Bioinformatics has since been applied all across biology and medicine, and has also transformed almost every avenue in neuroscience. Yet in neuropsychology, NI perspectives remain largely unrealized. Ironically, NI offers enormous potential to the essential praxis of neuropsychology - assessing cognitive behavior and relating cognition to neural systems. Neuroinformatics can be applied to neuropsychology as richly as it has been applied across the neurosciences. Neuroinformatics for Neuropsychology is the first book to explain the relevance and value of NI to neuropsychology. It systematically describes NI tools, applications and models that can enhance the efforts of neuropsychologists. It also describes the implications of NI for neuropsychology in the 21st century – fundamental shifts away from the conventional modes of research, practice and communication that have thus far characterized the field. One of the foremost experts on the subject: Illustrates the vital role NI is playing throughout the neurosciences. Provides a sampling of NI tools and applications in neuroscience research, and lays out current organization structures that support NI. Describes the lack of NI in neuropsychology, differentiates between NI systems for neuropsychology and conventional computerized assessment methods, and proposes criteria for neuropsychology-specific NI systems. Describes NI applications and models currently in use in neuropsychology, and NI models for neuropsychology that are being pioneered in phenomics research. Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology. Projects the future of neuropsychological research and practice in light of the new generation of the internet, Web 2.0, geared to collective knowledge building. A vital introduction to a profound technological practice, Neuroinformatics for Neuropsychology is important reading for clinical neuropsychologists, cognitive neuroscientists, behavioral neurologists, and speech-language pathologists. Researchers, clinicians, and graduate students interested in informatics for the brain-behavioral sciences will especially welcome this unique volume.


The OMICs

The OMICs

Author: Giovanni Coppola

Publisher: Oxford University Press

Published: 2014

Total Pages: 385

ISBN-13: 0199855455

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The OMICs:Applications in Neuroscience summarizes the state of the art in OMICs applications in neurology and neuroscience, attracting neurologists who are interested in the progress of this field towards clinical applications, and neuroscientists who may be not familiar with the most recent advances in this ever-changing field. The book will include an overview of most relevant high-throughput approaches (collectively known as 'OMICs') and how they relate to neurology and neuroscience. The explosion of high-throughput assays has introduced large datasets, computational servers, and bioinformatics approaches to neuroscience and medicine in general. The reader will be provided with an overview of the application or method, a perspective on the current and future applications in neurology and neuroscience, and a few published examples illustrating possible practical use. Emerging topics such as ethical issues related to personal genome sequencing, epigenetics, network analysis, and role of peripheral biomarkers in disease diagnosis and follow-up will be covered as well.


Mathematical Foundations of Neuroscience

Mathematical Foundations of Neuroscience

Author: G. Bard Ermentrout

Publisher: Springer Science & Business Media

Published: 2010-07-01

Total Pages: 434

ISBN-13: 0387877088

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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.


Bioinformatics of Behavior: Part 1

Bioinformatics of Behavior: Part 1

Author:

Publisher: Academic Press

Published: 2012-12-03

Total Pages: 0

ISBN-13: 9780123884084

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This issue of International Review of Neurobiology is split over 2 volumes, bringing together cutting-edge research on Bioinformatics of Behavior. The 2 volumes review current knowledge and understanding, provide a starting point for researchers and practitioners entering the field, and build a platform for further research and discovery.


An Introductory Course in Computational Neuroscience

An Introductory Course in Computational Neuroscience

Author: Paul Miller

Publisher: MIT Press

Published: 2018-10-09

Total Pages: 405

ISBN-13: 0262347563

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A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.


Computational Neuroscience: Theoretical Insights into Brain Function

Computational Neuroscience: Theoretical Insights into Brain Function

Author: Paul Cisek

Publisher: Elsevier

Published: 2007-11-14

Total Pages: 571

ISBN-13: 0080555020

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Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function.• Includes contributions by some of the most influential people in the field of computational neuroscience• Demonstrates how computational approaches are being used today to interpret experimental data• Covers a wide range of topics from single neurons, to neural systems, to abstract models of learning