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Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neu… Read more
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Immediately download your ebook while waiting for your print delivery. No promo code needed.
Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory.
Neuroscientists, experimental neuroscientists, computational neuroscientists, mathematicians
Chapter 1: Introduction
Chapter 2: The Passive Isopotential Cell
Chapter 3: Differential Equations
Chapter 4: The Active Isopotential Cell
Chapter 5: The Quasi-Active Isopotential Cell
Chapter 6: The Passive Cable
Chapter 7: Fourier Series and Transforms
Chapter 8: The Passive Dendritic Tree
Chapter 9: The Active Dendritic Tree
Chapter 10: Extracellular Potential
Chapter 11: Reduced Single Neuron Models
Chapter 12: Probability and Random Variables
Chapter 13: Synaptic Transmission and Quantal Release
Chapter 14: Neuronal Calcium SignalingNeuronal Calcium Signaling⁎
Chapter 15: Neurovascular Coupling, the BOLD Signal and MRI
Chapter 16: The Singular Value Decomposition and ApplicationsThe Singular Value Decomposition and Applications⁎
Chapter 17: Quantification of Spike Train Variability
Chapter 18: Stochastic Processes
Chapter 19: Membrane NoiseMembrane Noise*
Chapter 20: Power and Cross-Spectra
Chapter 21: Natural Light Signals and Phototransduction
Chapter 22: Firing Rate Codes and Early Vision
Chapter 23: Models of Simple and Complex Cells
Chapter 24: Models of Motion Detection
Chapter 25: Stochastic Estimation Theory
Chapter 26: Reverse-Correlation and Spike Train Decoding
Chapter 27: Signal Detection Theory
Chapter 28: Relating Neuronal Responses and Psychophysics
Chapter 29: Population CodesPopulation Codes⁎
Chapter 30: Neuronal Networks
Chapter 31: Solutions to Exercises
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