Principles of Computational Modelling in Neuroscience

Inbunden, Engelska, 2023

Av David Sterratt, Bruce Graham, Andrew Gillies, Gaute Einevoll, David Willshaw, David (University of Edinburgh) Sterratt, Bruce (University of Stirling) Graham, Andrew (Psymetrix Limited) Gillies, Gaute (Norwegian University of Life Sciences (NMBU)) Einevoll, David (University of Edinburgh) Willshaw

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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.

Produktinformation

Utgivningsdatum2023-10-05
Mått210 x 262 x 35 mm
Vikt1 350 g
FormatInbunden
SpråkEngelska
Antal sidor544
Upplaga2
FörlagCambridge University Press
ISBN9781108483148

Tillhör följande kategorier

Biovetenskap inom Naturvetenskap och teknik

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.

Preface; Acknowledgements; List of abbreviations 1. Introduction; 2. The basis of electrical activity in the neuron; 3. The Hodgkin–Huxley model of the action potential; 4. Models of active ion channels; 5. Modelling neurons over space and time; 6. Intracellular mechanisms; 7. The synapse; 8. Simplified models of the neuron; 9. Networks of neurons; 10. Brain tissue; 11. Plasticity; 12. Development of the nervous system; 13. Modelling measurements and stimulation; 14. Model selection and optimisation; 15. Farewell; References; Index.

'This new edition builds superbly on its predecessor. Expository excellence and beautifully clear figures remain, whilst extra material has been added throughout. New chapters cover critical topics such as modelling the way that neural signals are measured, and the details of model optimization and selection. Its impressive combination of depth and breadth makes the text perfect source material for a wide variety of courses.' Peter Dayan, Managing Director, Max-Planck Institute for Biological Cybernetics, Tuebingen