[Comp-neuro] New edition of Fundamental of Computational Neuroscience

Thomas Trappenberg tt at cs.dal.ca
Sat Nov 14 01:00:31 CET 2009

Dear Colleagues,

I am pleased to announce the publication of the second edition of my
book Fundamentals
of Computational
which contains major revisions and additions to the first edition. The book
now includes MATLAB examples in each chapter and some exercises. I hope that
this editon is more suited to introductory courses in theoretical

Regards, Thomas Trappenberg


*New to this edition*

   - To aid its use as a textbook, now includes simulation sections with
   program examples and sample exercises. Additional classroom resources are
   available on the web site for this book, including the figures in pdf
   format, example slides, and program versions for two open source
   alternatives to MATLAB, Octave and SciLab.
   - Features new and important system-level models, with all chapters
   revised throughout to take account of developments in the past seven years
   - Completely redesigned inside, with a clearer more textbook driven

Computational neuroscience is the theoretical study of the brain to uncover
the principles and mechanisms that guide the development, organization,
information processing, and mental functions of the nervous system. Although
not a new area, it is only recently that enough knowledge has been gathered
to establish computational neuroscience as a scientific discipline in its
own right. Given the complexity of the field, and its increasing importance
in progressing our understanding of how the brain works, there has long been
a need for an introductory text on what is often assumed to be an
impenetrable topic.

The new edition of Fundamentals of Computational Neuroscience build on the
success and strengths of the first edition. It introduces the theoretical
foundations of neuroscience with a focus on the nature of information
processing in the brain. The book covers the introduction and motivation of
simplified models of neurons that are suitable for exploring information
processing in large brain-like networks. Additionally, it introduces several
fundamental network architectures and discusses their relevance for
information processing in the brain, giving some examples of models of
higher-order cognitive functions to demonstrate the advanced insight that
can be gained with such studies.

*Table of Contents*

1: Introduction
*Basic Neurons*
2: Neurons and conductance-based models
3: Simplified neuron and population models
4: Associators and synaptic plasticity
*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
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