Mathematics for Neuroscientists
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29 666 Ft
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Product details:
- Publisher Elsevier Science
- Date of Publication 16 September 2010
- ISBN 9780123748829
- Binding Hardback
- No. of pages498 pages
- Size 276x215 mm
- Weight 1600 g
- Language English 0
Categories
Long description:
Virtually all scientific problems in neuroscience require mathematical analysis, and all neuroscientists are increasingly required to have a significant understanding of mathematical methods. There is currently no comprehensive, integrated introductory book on the use of mathematics in neuroscience; existing books either concentrate solely on theoretical modeling or discuss mathematical concepts for the treatment of very specific problems. This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their importance for neuroscience. All mathematical concepts will be introduced from the simple to complex using the most widely used computing environment, Matlab.
This book will provide a grounded introduction to the fundamental concepts of mathematics, neuroscience and their combined use, thus providing the reader with a springboard to cutting-edge research topics and fostering a tighter integration of mathematics and neuroscience for future generations of students.
MoreTable of Contents:
1 Introduction 2 The Passive Isopotential Cell 3 Differential Equations 4 The Active Isopotential Cell 5 The Quasi-Active Isopotential Cell 6 The Passive Cable 7 Fourier Series and Transforms 8 The Passive Dendritic Tree9 The Active Dendritic Tree 10 Reduced Single Neuron Models 11 Probability and Random Variables 12 Synaptic Transmission and Quantal Release 13 Neuronal Calcium Signaling14 The Singular Value Decomposition and Applications15 Quantification of Spike Train Variability 16 Stochastic Processes 17 Membrane Noise18 Power and Cross Spectra 19 Natural Light Signals and Phototransduction 20 Firing Rate Codes and Early Vision21 Models of Simple and Complex Cells 22 Stochastic Estimation Theory 23 Reverse-Correlation and Spike Train Decoding 24 Signal Detection Theory 25 Relating Neuronal Responses and Psychophysics 26 Population Codes 27 Neuronal Networks 28 Solutions to Selected Exercises
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