The task of understanding the principles of information processing in the brain poses, apart from numerous experimental questions, challenging theoretical problems on all levels from molecules to behavior. This books concentrates on modeling approaches at the level of neurons and small populations of neurons, since we think that this is an appropriate level to address fundamental questions of neuronal coding, signal transmission, or synaptic plasticity. In this text we concentrate on theoretical concepts and phenomenological models derived from them. We think of a neuron primarily as a dynamic element that emits output pulses whenever the excitation exceeds some threshold. The resulting sequence of pulses or “spikes” contains all the information that is transmitted from one neuron to the next. In order to understand signal transmission and signal processing in neuronal systems, we need an understanding of their basic elements, i.e., the neurons, which is the topic of Part I. New phenomena emerge when several neurons are coupled. Part II introduces network concepts, in particular pattern formation, collective excitations, and rapid signal transmission between neuronal populations. Learning concepts presented in Part III are based on spike-time-dependent synaptic plasticity.

We wrote this book as an introduction to spiking neuron models for advanced undergraduate or graduate students. It can be used either as the main text for a course that focuses on neuronal dynamics, or as part of a larger course in computational neuroscience, theoretical biology, neuronal modeling, biophysics, or neural networks.