Fundamentals of Computational Neuroscience

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