Treffer: Fundamentals of Computational Neuroscience: Third Edition

This is an introductory book for computational neuroscience. The book starts with a high-level overview and some fundamental questions about brain theories, a brief discussion about the role of modeling, and some other basic facts from neuroscience. The book also reviews essential scientific programming in Python and the basic mathematical and statistical concept used in the book. The following part of the book focuses on basic mechanisms and modeling of single neurons or population averages. This starts from detailed discussion of changes in the membrane potentials through ion channels, spike generations, and synaptic plasticity, with increasingly abstractions in the following chapters. After this, the information processing capabilities of basic networks are described, including feedforward and competitive recurrent networks. The last part of the book describes some examples of combining such elementary networks as well as some examples of more system-level models of the brain. This new edition of my book incorporates recent lessons from deep learning. While there are excellent books on deep learning, the emphasis here is their connection to brain processing. An important aspect is thereby the concepts of representational learning and computation with uncertainties. Also, I now included gated recurrent neural networks that are becoming an important fundamental mechanism when thinking about brain processing. While we will not be able to dive into all the recent progress, I hope that the text will guide further specific studies and research.