Biophysics of Computation: Information Processing in Single Neurons / Edition 1

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More About This Textbook


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.
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Product Details

  • ISBN-13: 9780195181999
  • Publisher: Oxford University Press, USA
  • Publication date: 10/28/2004
  • Series: Computational Neuroscience Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 588
  • Product dimensions: 9.20 (w) x 6.40 (h) x 1.10 (d)

Table of Contents

List of Symbols
Introduction 1
1 The Membrane Equation 5
2 Linear Cable Theory 25
3 Passive Dendritic Trees 49
4 Synaptic Input 85
5 Synaptic Interactions in a Passive Dendritic Tree 117
6 The Hodgkin - Huxley Model of Action Potential Generation 142
7 Phase Space Analysis of Neuronal Excitability 172
8 Ionic Channels 193
9 Beyond Hodgkin and Huxley: Calcium and Calcium-Dependent Potassium Currents 212
10 Linearizing Voltage-Dependent Currents 232
11 Diffusion, Buffering, and Binding 248
12 Dendritic Spines 280
13 Synaptic Plasticity 308
14 Simplified Models of Individual Neurons 330
15 Stochastic Models of Single Cells 350
16 Bursting Cells 374
17 Input Resistance, Time Constants, and Spike Initiation 381
18 Synaptic Input to a Passive Tree 402
19 Voltage-Dependent Events in the Dendritic Tree 428
20 Unconventional Computing 452
21 Computing with Neurons: A Summary 469
App. A Passive Membrane Parameters 481
App. B A Miniprimer on Linear Systems Analysis 484
App. C Sparse Matrix Methods for Modeling Single Neurons 487
References 503
Index 553
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