Research Catalog

Details

Description

1. 384 pages : illustrations; 24 cm.

Summary

1. "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. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can 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 serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics."--BOOK JACKET.

Series statement

1. Computational neuroscience series

Uniform title

1. Computational neuroscience.

Subject

1. Neural conduction
2. Neural networks (Neurobiology)
3. Computational neuroscience
4. Neurons
5. Action potentials (Electrophysiology)

Contents

1. 1. 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 Computing -- 21. Computing with Neurons: A Summary -- App. A. Passive Membrane Parameters -- App. B. A Miniprimer on Linear Systems Analysis -- App. C. Sparse Matrix Methods for Modeling Single Neurons.

Owning institution

1. Columbia University Libraries

Note

1. Originally published: 1998.

Bibliography (note)

1. Includes bibliographical references and index.