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.

It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.

This is the first book that attempts to bring together what is known about the fundamental mechanisms that underlie the development of the cortex in mammals. Ranging from the emergence of the forebrain from the neural plate to the functioning adult form, the authors draw on evidence from several species to provide a detailed description of processes at each stage. Where appropriate, evidence is extrapolated from non-mammalian species to generate hypotheses about mammalian development. In contrast to other texts of developmental biology, Mechanisms of Cortical Development integrates information on regulatory processes at the levels of molecules, cells and metworks. The authors draw together an extensive literature on cellular development and structural morphology, biochemical and genetic events and hypotheses that have been subject to mathematical modelling. Important metholdogies, such as transgenics and formal modelling, are explained for the non-specialist. Major future challenges are clearly identified. This is a unique contribution to the literature, combining the fundamentals of experimental developmental neurobiology with accessible neural modelling. It will be essential reading for neuroscientists in general as well as those with a particular interest in development.

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.