MATLAB for Neuroscientists
An Introduction to Scientific Computing in MATLAB
Series: International Review of Cell and Molecular Biology; 269;
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Product details:
- Publisher Elsevier Science
- Date of Publication 6 November 2008
- ISBN 9780123745514
- Binding Hardback
- No. of pages400 pages
- Size 234x190 mm
- Weight 925 g
- Language English 0
Categories
Long description:
"
MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as ""black boxes"".
Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research.
" MoreTable of Contents:
1. Introduction2. MATLAB Tutorial3. Visual Search and Pop Out4. Attention5. Psychophysics6. Signal Detection Theory7. Frequency Analysis Part I: Fourier Decomposition8. Frequency Analysis Part II: Nonstationary Signals and Spectrograms9. Wavelets10. Convolution11. Introduction to Phase Plane Analysis12. Exploring the Fitzhugh-Nagumo Model13. Neural Data Analysis: Encoding14. Principal Components Analysis15. Information Theory16. Neural Decoding Part I: Discrete Variables17. Neural Decoding Part II: Continuous Variables18. Functional Magnetic Imaging19. Voltage-Gated Ion Channels20. Models of a Single Neuron21. Models of the Retina22. Simplified Model of Spiking Neurons23. Fitzhugh-Nagumo Model: Traveling Waves24. Decision Theory25. Markov Models26. Modeling Spike Trains as a Poisson Process27. Synaptic Transmission28. Neural Networks Part I: Unsupervised Learning29. Neural Network Part II: Supervised Learning
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