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How do single nerve cells multiply, integrate or delay synaptic inputs? What is the repertoire of computational operations available to individual nerve cells? How accurately can information be encoded in the voltage across the membrane, in the intracellular calcium concentration or in the timing of individual spikes? "Biophysics of Computation: Information Processing in Single Neurons" focuses on these questions at the interface between cellular biophysics and computational theory. Neural network research builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites and voltage-dependent ionic currents. This textbook rectifies this situation.

Key topics are the linear cable equation, passive dendritic trees and dendritic spines, chemical and electrical synapses and how to treat them from a computational point of view, nonlinear interactions in passive and active dendritic trees, the Hodgkin-Huxley model of action potential generation and propagation, phase space analysis, linking stochastic ionic channels to membrane dependent currents, calcium- and potassium-currents and their role in information processing, the role of diffusion, buffering and binding of calcium and other messenger systems in information processing and storage, short- and long-term models of synaptic plasticity, simplified models of single cells, stochastic aspects of neuronal firing, the nature of the neuronal code and unconventional models of computation involving molecules, puffs of gas or neuropeptides.

Each chapter ends with a recapitulation of the material presented. The ultimate chapter provides a summary view of neuron-style computation, ending with a list of strategic questions for research. The text speaks to undergraduate and graduate students and beyond in the neuroscience, electrical and computer engineering and physics communities.