Principles of computational modelling in neuroscience
Sterratt, David
Principles of computational modelling in neuroscience David Sterratt ... [et al.]. - New York : Cambridge University Press, ©2011. - xi, 390 p. : ill. (some col.) ; 26 cm.
Includes bibliographical references (p. [351]- and index.
"The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience"-- "This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons. It is designed to be read by people from a wide range of backgrounds from the biological, physical and computational sciences. The word 'model' can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning. For example, to biologists, the term 'model' can mean 'animal model'; to physicists, the standard model is a step towards a complete theory of fundamental particles and interactions. We therefore start this chapter by attempting to clarify what we mean by computational models and modelling in the context of neuroscience. Before giving a brief chapter-by-chapter overview of the book, we also discuss what might be called the philosophy of modelling: general issues in computational modelling that recur throughout the book"--
9780521877954
2011001055
101569919 DNLM
Computational neuroscience.
Models, Neurological.
Computer Simulation.
Neural Conduction.
Synaptic Transmission.
QP357.5 / .P75 2011
612.801 / STE-P
2011 J-499 WL 20
Principles of computational modelling in neuroscience David Sterratt ... [et al.]. - New York : Cambridge University Press, ©2011. - xi, 390 p. : ill. (some col.) ; 26 cm.
Includes bibliographical references (p. [351]- and index.
"The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience"-- "This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons. It is designed to be read by people from a wide range of backgrounds from the biological, physical and computational sciences. The word 'model' can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning. For example, to biologists, the term 'model' can mean 'animal model'; to physicists, the standard model is a step towards a complete theory of fundamental particles and interactions. We therefore start this chapter by attempting to clarify what we mean by computational models and modelling in the context of neuroscience. Before giving a brief chapter-by-chapter overview of the book, we also discuss what might be called the philosophy of modelling: general issues in computational modelling that recur throughout the book"--
9780521877954
2011001055
101569919 DNLM
Computational neuroscience.
Models, Neurological.
Computer Simulation.
Neural Conduction.
Synaptic Transmission.
QP357.5 / .P75 2011
612.801 / STE-P
2011 J-499 WL 20