Principles of Computational Modelling in Neuroscience (Hardcover)

David Sterratt

ISBN 10: 1108483143 / ISBN 13: 9781108483148

Verlag: Cambridge University Press, Cambridge, 2023

Beschreibung

Hardcover. Taking a step-by-step approach to modelling neurons and neural circuitry, this textbook teaches students how to use computational techniques to understand the nervous system at all levels, using case studies throughout to illustrate fundamental principles. Starting with a simple model of a neuron, the authors gradually introduce neuronal morphology, synapses, ion channels and intracellular signalling. This fully updated new edition contains additional examples and case studies on specific modelling techniques, suggestions on different ways to use this book, and new chapters covering plasticity, modelling extracellular influences on brain circuits, modelling experimental measurement processes, and choosing appropriate model structures and their parameters. The online resources offer exercises and simulation code that recreate many of the book's figures, allowing students to practice as they learn. Requiring an elementary background in neuroscience and high-school mathematics, this is an ideal resource for a course on computational neuroscience. Providing a step-by-step and practical account of how to model neurons and neural circuitry, this textbook is designed for advanced undergraduate and postgraduate students of computational neuroscience as well as for researchers in neuroscience and related sciences wishing to apply computational approaches to interpret data and make predictions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9781108483148

Bibliografische Details

• Titel: Principles of Computational Modelling in Neuroscience (Hardcover)
• Verlag: Cambridge University Press, Cambridge
• Veröffentlichungsdatum: 2023
• Sprache: Englisch
• ISBN-10: 1108483143
• ISBN-13: 9781108483148
• Bindung: Hardcover
• Ausgabe: 2. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

Taking a step-by-step approach to modelling neurons and neural circuitry, this textbook teaches students how to use computational techniques to understand the nervous system at all levels, using case studies throughout to illustrate fundamental principles. Starting with a simple model of a neuron, the authors gradually introduce neuronal morphology, synapses, ion channels and intracellular signalling. This fully updated new edition contains additional examples and case studies on specific modelling techniques, suggestions on different ways to use this book, and new chapters covering plasticity, modelling extracellular influences on brain circuits, modelling experimental measurement processes, and choosing appropriate model structures and their parameters. The online resources offer exercises and simulation code that recreate many of the book's figures, allowing students to practice as they learn. Requiring an elementary background in neuroscience and high-school mathematics, this is an ideal resource for a course on computational neuroscience.

Über die Autorinnen und Autoren

David Sterratt is Lecturer and Deputy Director of Learning and Teaching in the Institute for Adaptive and Neural Computation, School of Informatics, at the University of Edinburgh. He developed material for this book while teaching computational neuroscience to informatics, neuroscience, and neuroinformatics masters students. He has developed and maintains several scientific software packages.

Bruce Graham is Emeritus Professor in Computing Science in the Faculty of Natural Sciences at the University of Stirling. He has been a researcher in computational neuroscience for more than 30 years and has served as a board member of the Organisation of Computational Neurosciences.

Andrew Gillies is Chief Technology Officer of Grid Software at GE Vernova. He has been actively involved in computational neuroscience research and his simulation model of the subthalamic nucleus projection neuron is recognised as a standard. He he has taught neuroscience modelling at Master's and Ph.D. level.

Gaute Einevoll is Professor of Physics at the Norwegian University of Life Sciences and the University of Oslo, working on modelling of nerve cells, networks of nerve cells, brain tissue, brain signals and development of neuroinformatics software tools, including LFPy.

David Willshaw is Emeritus Professor of Computational Neurobiology in the Institute for Adaptive and Neural Computation at the University of Edinburgh, where he led the innovative doctoral training programme in neuroinformatics and computational neuroscience. With over 40 years' research experience, he has received several awards including, most recently, the Braitenberg Award in Computational Neuroscience.