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Computational Neuroscience: A First Course
Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in neuroscience with backgrounds in biology, psychology and medicine, takes its readers on a journey through three fundamental domains of computational membrane biophysics, systems theory and artificial neural networks. The required mathematical concepts are kept as intuitive and simple as possible throughout the book, making it fully accessible to readers who are less familiar with mathematics. Overall, Computational Neuroscience - A First Course represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience.
146 pages, Paperback
First published June 5, 2013
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August 16, 2015
A quite short introduction to computational/theoretical neuroscience. As such the range of topics covered is rather limited. I haven't yet read a different book about computational neuroscience, but if you want to have more than just a shallow look at the topic you're probably better off skipping this book.
The quality of the explanations/derivations in this book are overall OK to good. Given some time to think things through I was mostly able to follow the lines of thought, and felt like I had a decent understanding of the subject matter at hand. In some places the explanations given are somewhat sparse.
The main problem I have with this book, is that there are several sections, that feel "free-floating", because they do not build directly on previous parts and there is little to no explanation as to why the presented topic is important. This may be alleviated by using the book primarily as supplementary material to a lecture as opposed to as a standalone textbook.
Brief synopsis of chapters:
1: An introduction to modelling single cell action potentials with the Hodgkin-Huxley model and seeing the cell as an electric circuit for propagating signals/currents.
2: Receptive fields and tuning of cells to stimuli.
3: "Fourier Analysis for Neuroscientists". A nice introduction to Fourier analysis, but almost completely missing actual applications and purposes for FA in neuroscience ("free-floating").
4: An overview over the different types of artificial neural networks and their learning rules.
5: How information is coded in the brain/neural networks.
Personally, I come from a computer science background. As such I found chapters 1 and 5 most interesting. Chapter 4 was mostly repitition of things I already knew, but gave some new perspectives on the topic.
The quality of the explanations/derivations in this book are overall OK to good. Given some time to think things through I was mostly able to follow the lines of thought, and felt like I had a decent understanding of the subject matter at hand. In some places the explanations given are somewhat sparse.
The main problem I have with this book, is that there are several sections, that feel "free-floating", because they do not build directly on previous parts and there is little to no explanation as to why the presented topic is important. This may be alleviated by using the book primarily as supplementary material to a lecture as opposed to as a standalone textbook.
Brief synopsis of chapters:
1: An introduction to modelling single cell action potentials with the Hodgkin-Huxley model and seeing the cell as an electric circuit for propagating signals/currents.
2: Receptive fields and tuning of cells to stimuli.
3: "Fourier Analysis for Neuroscientists". A nice introduction to Fourier analysis, but almost completely missing actual applications and purposes for FA in neuroscience ("free-floating").
4: An overview over the different types of artificial neural networks and their learning rules.
5: How information is coded in the brain/neural networks.
Personally, I come from a computer science background. As such I found chapters 1 and 5 most interesting. Chapter 4 was mostly repitition of things I already knew, but gave some new perspectives on the topic.
Displaying 1 of 1 review