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Principles of Computational Modelling in Neuroscience [Kõva köide]

3.93/5 (21 hinnangut Goodreads-ist)
(University of Stirling), , (University of Edinburgh), (University of Edinburgh)
  • Formaat: Hardback, 404 pages, kõrgus x laius x paksus: 253x194x23 mm, kaal: 1020 g, 7 Tables, black and white; 91 Halftones, unspecified; 87 Line drawings, unspecified
  • Ilmumisaeg: 30-Jun-2011
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521877954
  • ISBN-13: 9780521877954
Teised raamatud teemal:
Principles of Computational Modelling in NeuroscienceSuurem pilt
  • Formaat: Hardback, 404 pages, kõrgus x laius x paksus: 253x194x23 mm, kaal: 1020 g, 7 Tables, black and white; 91 Halftones, unspecified; 87 Line drawings, unspecified
  • Ilmumisaeg: 30-Jun-2011
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521877954
  • ISBN-13: 9780521877954
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780521877954.html
Märksõnad:
"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 ofneuronal 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 'animalmodel'; 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"--

Provided by publisher.

Arvustused

'Here at last is a book that is aware of my problem, as an experimental neuroscientist, in understanding the maths I expect it to be as mind expanding as my involvement with its authors was over the years. I only wish I had had the whole book sooner - then my students and post-docs would have been able to understand what I was trying to say and been able to derive the critical tests of the ideas that only the rigor of the mathematical formulation of them could have generated.' Gordon W. Arbuthnott, Okinawa Institute of Science and Technology, Japan 'This is a wonderful, clear and compelling text on mathematically-minded computational modelling in neuroscience. It is beautifully aimed at those engaged in capturing quantitatively, and thus simulating, complex neural phenomena at multiple spatial and temporal scales, from intracellular calcium dynamics and stochastic ion channels, through compartmental modelling, all the way to aspects of development. It takes particular care to define the processes, potential outputs and even some pitfalls of modelling, and can be recommended for containing the key lessons and pointers for people seeking to build their own computational models. By eschewing issues of coding and information processing, it largely hews to concrete biological data, and it nicely avoids sacrificing depth for breadth. It is very suitably pitched as a master's level text, and its two appendices, on mathematical methods and software resources, will rapidly become dog-eared.' Peter Dayan, University College London 'Principles of Computational Modelling in Neuroscience sets a new standard of clarity and insight in explaining biophysical models of neurons. This provides a firm foundation for network models of brain function and brain development. I plan to use this textbook in my course on computational neurobiology.' Terrence Sejnowski, Salk Institute for Biological Studies and University of California, San Diego

Muu info

This book explains how to use techniques of computational modelling to understand the nervous system at all levels from ion channels to networks.
List of abbreviations
viii
Preface x
Acknowledgements xii
Chapter 1 Introduction
1(12)
1.1 What is this book about?
1(8)
1.2 Overview of the book
9(4)
Chapter 2 The basis of electrical activity in the neuron
13(34)
2.1 The neuronal membrane
14(2)
2.2 Physical basis of ion movement in neurons
16(6)
2.3 The resting membrane potential: the Nernst equation
22(4)
2.4 Membrane ionic currents not at equilibrium: the Goldman-Hodgkin-Katz equations
26(4)
2.5 The capacitive current
30(1)
2.6 The equivalent electrical circuit of a patch of membrane
30(5)
2.7 Modelling permeable properties in practice
35(1)
2.8 The equivalent electrical circuit of a length of passive membrane
36(3)
2.9 The cable equation
39(6)
2.10 Summary
45(2)
Chapter 3 The Hodgkin-Huxley model of the action potential
47(25)
3.1 The action potential
47(3)
3.2 The development of the model
50(10)
3.3 Simulating action potentials
60(5)
3.4 The effect of temperature
65(1)
3.5 Building models using the Hodgkin-Huxley formalism
66(5)
3.6 Summary
71(1)
Chapter 4 Compartmental models
72(24)
4.1 Modelling the spatially distributed neuron
72(1)
4.2 Constructing a multi-compartmental model
73(4)
4.3 Using real neuron morphology
77(6)
4.4 Determining passive properties
83(4)
4.5 Parameter estimation
87(6)
4.6 Adding active channels
93(2)
4.7 Summary
95(1)
Chapter 5 Models of active ion channels
96(37)
5.1 Ion channel structure and function
97(2)
5.2 Ion channel nomenclature
99(4)
5.3 Experimental techniques
103(2)
5.4 Modelling ensembles of voltage-gated ion channels
105(5)
5.5 Markov models of ion channels
110(5)
5.6 Modelling ligand-gated channels
115(3)
5.7 Modelling single channel data
118(6)
5.8 The transition state theory approach to rate coefficients
124(7)
5.9 Ion channel modelling in theory and practice
131(1)
5.10 Summary
132(1)
Chapter 6 Intracellular mechanisms
133(39)
6.1 Ionic concentrations and electrical response
133(1)
6.2 Intracellular signalling pathways
134(3)
6.3 Modelling intracellular calcium
137(1)
6.4 Transmembrane fluxes
138(2)
6.5 Calcium stores
140(3)
6.6 Calcium diffusion
143(8)
6.7 Calcium buffering
151(8)
6.8 Complex intracellular signalling pathways
159(4)
6.9 Stochastic models
163(6)
6.10 Spatial modelling
169(1)
6.11 Summary
170(2)
Chapter 7 The synapse
172(24)
7.1 Synaptic input
172(1)
7.2 The postsynaptic response
173(6)
7.3 Presynaptic neurotransmitter release
179(8)
7.4 Complete synaptic models
187(2)
7.5 Long-lasting synaptic plasticity
189(2)
7.6 Detailed modelling of synaptic components
191(1)
7.7 Gap junctions
192(2)
7.8 Summary
194(2)
Chapter 8 Simplified models of neurons
196(30)
8.1 Reduced compartmental models
198(6)
8.2 Integrate-and-fire neurons
204(7)
8.3 Making integrate-and-fire neurons more realistic
211(7)
8.4 Spike-response model neurons
218(2)
8.5 Rate-based models
220(4)
8.6 Summary
224(2)
Chapter 9 Networks of neurons
226(41)
9.1 Network design and construction
227(6)
9.2 Schematic networks: the associative memory
233(10)
9.3 Networks of simplified spiking neurons
243(8)
9.4 Networks of conductance-based neurons
251(3)
9.5 Large-scale thalamocortical models
254(5)
9.6 Modelling the neurophysiology of deep brain stimulation
259(6)
9.7 Summary
265(2)
Chapter 10 The development of the nervous system
267(47)
10.1 The scope of developmental computational neuroscience
267(2)
10.2 Development of nerve cell morphology
269(10)
10.3 Development of cell physiology
279(1)
10.4 Development of nerve cell patterning
280(4)
10.5 Development of patterns of ocular dominance
284(2)
10.6 Development of connections between nerve and muscle
286(8)
10.7 Development of retinotopic maps
294(18)
10.8 Summary
312(2)
Chapter 11 Farewell
314(5)
11.1 The development of computational modelling in neuroscience
314(1)
11.2 The future of computational neuroscience
315(3)
11.3 And finally...
318(1)
Appendix A Resources
319(9)
A.1 Simulators
319(5)
A.2 Databases
324(2)
A.3 General-purpose mathematical software
326(2)
Appendix B Mathematical methods
328(23)
B.1 Numerical integration methods
328(5)
B.2 Dynamical systems theory
333(8)
B.3 Common probability distributions
341(5)
B.4 Parameter estimation
346(5)
References 351(31)
Index 382
David Sterratt is a Research Fellow in the School of Informatics at the University of Edinburgh. His computational neuroscience research interests include models of learning and forgetting, and the formation of connections within the developing nervous system. Bruce Graham is a Reader in Computing Science in the Department of Computing Science and Mathematics at the University of Stirling. Focusing on computational neuroscience, his research covers nervous system modelling at many levels. Andrew Gillies works at Psymetrix Limited, Edinburgh. He has been actively involved in computational neuroscience research. David Willshaw is Professor of Computational Neurobiology in the School of Informatics at the University of Edinburgh. His research focuses on the application of methods of computational neurobiology to an understanding of the development and functioning of the nervous system.