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The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the previous editions. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.
Thomas P. Trappenberg is a Professor of Computer Science at Dalhousie University. He has also been a Research Fellow at Riken Brain Science Institute and a Research Officer at Oxford University. He has previously published Fundamentals of Computational Neuroscience, First Ed., Fundamentals of Computational Neuroscience, Second Ed. and Fundamentals of Machine Learning.
1: Introduction
Basic Neurons
2: Neurons and conductance-based models
3: Simplified neuron and population models
4: Associators and synaptic plasiticity
Basic Networks
5: Cortical organizations and simple networks
6: Feed-forward mapping networks
7: Cortical feature maps and competitive population coding
8: Recurrent associative networks and episodic memory
System-Level Models
9: Modular networks, motor control, and reinforcement learning
10: The cognitive brain
A: Some useful mathematics
B: Numerical calculus
C: Basic probability theory
D: Basic information theory
E: A brief introduction to MATLAB
