We are very pleased to announce our new book:
Principles of Computational Modelling in Neuroscience
2nd edition, Cambridge University Press, October 2023
Hardback, paperback and ebook.
URL: www.cambridge.org/highereducation/isbn/9781108716420
David Sterratt, University of Edinburgh U.K.
Bruce Graham, University of Stirling U.K.
Andrew Gillies, Psymetrix Limited
Gaute Einevoll, Norwegian University of Life Sciences (NMBU)
David Willshaw, University of Edinburgh U.K.
Taking a step-by-step approach to modelling neurons and neuralcircuitry, 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.
Key features:
Presents all models within the context of biological scenarios to connect computational
models with real neurobiology.
Highlights and explains mathematical details in boxes alongside the main text,
so readers can follow the discussion easily and clearly.
Shows how to choose an appropriate model structure and to set its parameter values.
Demonstrates how to translate a mathematical formulation of a model into a simulation.