Biophysics of Computation: Information Processing in Single Neurons

Biophysics of Computation: Information Processing in Single Neurons

by Christof Koch
Oxford University Press: New York, New York, 1999. 562 pages and 221 illustrations.
ISBN 0-19-510491-9


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 single-authored textbook focuses on the repertoire of computational operations available to individual nerve cells.

Table of Contents1. The Membrane Equation
2. Linear Cable Theory
3. Passive Dendritic Trees
4. Synaptic Input
5. Synaptic Interactions in a Passive Dendritic Tree
6. The Hodgkin-Huxley Model of Action-Potential Generation
7. Phase Space Analysis of Neuronal Excitability
8. Ionic Channels
9. Beyond Hodgkin and Huxley: Calcium, and Calcium-Dependent Potassium Currents
10. Linearizing Voltage-Dependent Currents
11. Diffusion, Buffering, and Binding
12. Dendritic Spines
13. Synaptic Plasticity
14. Simplified Models of Individual Neurons
15. Stochastic Models of Single Cells
16. Bursting Cells
17. Input Resistance, Time Constants, and Spike Initiation
18. Synaptic Input to a Passive Tree
19. Voltage-Dependent Events in the Dendritic Tree
20. Unconventional Coupling
21. Computing with Neurons — A Summary
Appendix A: Passive Membrane Parameters
Appendix B: A Minimprimer on Linear Systems Analysis
Appendix C: Sparse Matrix Methods for Modeling Single Neurons

Each chapter ends with a recapitulation of the material presented. The ultimate chapter presents a summary view of “neuron-style” computation and a list of strategic questions for research.

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