Spiking Neuron Models
Spiking Neuron Models Books
Product Description
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. Even as the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.
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very well written, simple to know, walks you through the judgment of each part of each equation. builds up more and more complex models based upon the previous models. You’ll learn a lot of matter-of-fact neurobiology stuff other than just modeling too.
Rating: 5 / 5
This is a very impressive book. It covers in a systematic manner a broad part of the field of theoretical neuroscience. It covers topics from models of single spiking neurons, through networks of interconnected neurons and up to neuronal plasticity. This book is also written very well. The style of this book reflects the background of the authors as Physicists; it therefore strives for simplicity wherever possible.
I used chapters from this book as a basis for some of my lectures in a course I teach: Introduction to Theoretical/Computational Neuroscience, a graduate level course. I especially liked the systematic approach they have adopted for describing various simplifications of the Hodgkin-Huxley equations.
Rating: 5 / 5
I have used this book as an introduction and reference book for modeling neurons since I ongoing my thesis work in computational neuroscience two years ago. It covers various types of spiking neuron models (e.g. Hodgkin-Huxley, Morris-Lecar, Integrate&Fire, Spike-Response-Develop), blast in neuron models, populace models, and plasticity/learning.
With this book and some programming skills, one has a solid foundation for modeling neurons on various levels.
It is a very helpful book, clearly written and comprehensive, providing sufficient detail and background in rank. Derivations of the equations are clearly presented and understandable to anyone with a clad knowledge of mathematics. A degree in physics is not required in order to read this book
I also like the literature recommendations at the end of each chapter, they give a excellent overview over vital original papers and further reviews.
I would strongly urge this book to undergraduate and PhD-students in computational neuroscience, as well as to anyone interested in modeling neurons.
Rating: 5 / 5