Table of Contents

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.

Promotional Information

Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.

About the Author

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.

Reviews

'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

'Principles of Computational Modelling in Neuroscience has long been my choice for my students from various backgrounds, but now it is even better! With the addition of comprehensive coverage for modelling extracellular activity, neural plasticity, and experimental stimulation and measurements, this text offers everything that my students need for a foundation in computational neuroscience. The new chapter on Model Section and Optimisation makes a difficult topic easy to understand. Principles of Computational Modelling in Neuroscience remains the best choice of a text that is rigorous but accessible to students from a variety of backgrounds.' Sharon Crook, Arizona State University

'I am thrilled to endorse the second edition of Principles of Computational Modelling in Neuroscience. This comprehensive and well-written text is an engaging resource on the fundamentals of biophysical modelling and computational neuroscience. The range of topics and clarity of exposition provides a spectacular display of the power of computational techniques to provide insight into the mysteries of central nervous system function across its diverse hierarchical levels. This second edition contains many additional examples and new chapters on synaptic plasticity and learning, modelling of experimental measurements, as well as model selection and optimization. The authors have a remarkable ability to explain complex concepts in a clear and accessible manner, making this book an ideal choice for both students and researchers. The gradually increasing sophistication of each chapter makes it highly suited as a textbook for an introductory undergraduate course focusing on computational aspects of cell and systems neuroscience.' Greg Conradi Smith, William & Mary, Virginia and author of Cellular Biophysics and Modeling (Cambridge, 2019)

'A valuable resource for both students and practitioners of computational modelling in neuroscience. The authors provide a step-by-step guide to modelling, from simple neurons to complex brain tissue. Each model is carefully explained and illustrated, put into context and critiqued. A definitive text for anyone wanting to develop their own models of neural circuitry!' Rosemary Fricker, Keele University Medical School; and Wolfson College, University of Cambridge

'This textbook is useful for engineers, mathematicians, biologists and computer scientists. It provides a deep understanding of neuron computational modelling. The approach used, from ion channels to the new plasticity chapter, provides the student with a full picture of neural network behaviour. Furthermore, this new edition includes some cases of studies where the concepts are applied to real experimental data.' Fernando Perez-Peña, University of Cadiz

'We have been using the book by Sterratt et al. for many years on our courses in theoretical neuroscience, to teach the basics of biophysics. The book does an excellent job of providing the basics in a highly understandable way for beginners in the field with lots of practical examples and additional background material for the implementation of models. The new edition of the book is even more useful since it integrates a lot of new material: nonlinear dynamics and bifurcations, neural field models, the modelling of measurement methods, and optimization. In addition, programming examples have been added, making the book even more useful for teaching.' Martin Giese, University of Tuebingen

'This book is an invaluable resource for computational neuroscientists, particularly for students starting their doctoral research. The comprehensive coverage, clear prose and extensive examples make it easy for a newcomer with only basic background knowledge to get to grips with a wide variety of complex computational modelling topics as a preparation for starting their own investigations.' Abigail Morrison, RWTH Aachen University