Biophysics of Computation: Information Processing in Single Neurons

Neural network research often 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 the situation by focusing on the repertoire of computational operations available to individual nerve cells. The author suggests how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, and in the timing of individual spikes, or nerve impulses. Key topics include the linear cable operation, 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 of 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 neuronal code and unconventional models of computation involving molecules, puffs of gas, or neuropeptides. Each chapter ends with a recapitulation of the material presented, and the ultimate chapter presents a summary view of 'neuron-style' computation, ending with a list of strategic questions for research.