Neural modeling : electrical signal processing in the nervous system
Ronald J. MacGregor, Edwin R. Lewis · Medical Entomology and Zoology · 1977
I. Introduction.- 1 Signal Processing in Nervous Systems.- 1.1. Neurons.- 1.2. Modeling.- 2 The Idealized Standard Neuron.- 2.1. The Idealized Standard Neuron.- II. Neural Modeling: Models of Excitation And Conduction.- 3 Models of Passive Membrane.- 3.1. The Nernst-Planck Model.- 3.2. The Einstein Relation.- 3.3. The Nernst Equation and Ionic Reversal Potentials.- 3.4. Space-Charge Neutrality.- 3.5. Nernst-Planck Membranes with Space-Charge Neutrality and Obeying the Einstein Relation.- 3.6. Space-Charge Neutrality in a Homogeneous Nernst-Planck Membrane.- 3.7. Sources of Permanent Potential Differences across Membranes.- 3.8. Impedance to Ion Flow at Donnan Jumps.- 3.9. Ion-Concentration and Electrical Potential Profiles for Various Membrane Models.- 3.10. Summary: A Quantitative Model of Neuronal Membrane.- 4 Equivalent Circuits for Passive Membrane.- 4.1. The Basic Equivalent Circuit for a Patch of Membrane.- 4.2. Small-Signal Equivalent Circuits.- 4.3. Equivalent Circuits for Large Signals.- 4.4. The Frankenhaeuser-Hodgkin Space.- 4.5. Summary.- 5 Models of Signal Generation in Neural Elements.- 5.1. Some General Considerations.- 5.2. The Eccles Model of Chemical Synapse.- 5.3. Bullock's Degrees of Freedom for a Chemical Synapse.- 5.4. The Quantum Model of Transmitter Release.- 5.5. Discrete Inputs to Other Receptors.- 5.6. Reliable Detection of Weak Signals in the Presence of Noise.- 5.7. The Fuortes-Hodgkin Model Ill.- 5.8. Spontaneous Activity in Neurons.- 6 Models of Distributed Passive Membrane.- 6.1. The Basic Model.- 6.2. Dipole Annihilation and Redistribution.- 6.3. Continuous Model for Response Spread over Neuronal Fibers.- 6.4. Continuous Analysis of the Uniform, Passively Conducting Fiber with Time-Invariant Parameters.- 6.5. Continuous Analysis of Branching Dendritic.- Trees: Rail's Equivalent Cylinder Model.- 6.6. Spatially Discrete Analysis of Passively Conducting Fibers and Fiber Trees.- 6.7. Shapes of Passively Conducted Signals.- 6.8. Conduction of Signals to Very Remote Sites.- 7 Models of Spike Generation and Conduction.- 7.1. The Iron Wire (or Heathcote-Lillie) Model.- 7.2. Threshold and Accommodation (or the Hill-Rashevsky-Monnier Model).- 7.3. The Hodgkin-Huxley Model.- 7.4. Abstractions of the Hodgkin-Huxley Model.- 7.5. Conduction of Spikes.- 7.8. Concluding Remarks.- III. Neural Coding: Models of Electrical Signal Processing.- 8 Neuromimes.- 9 Stochastic Models of Neuron Activity.- 9.1. Gerstein's Model.- 9.2. More General Models.- 10 Statistical Analysis of Neuronal Spike Trains.- 10.1. Statistical Measures for Single Trains.- 10.2. Statistical Measures for Simultaneously Recorded Trains.- 10.3. Applications of Neuronal Spike Train Analysis.- 11 Models of Neuron Pools.- 12 Models of Large Networks: Analytic Approaches.- 13 Models of Large Networks: Computer-Oriented Approaches.- 14 Models of Field Potentials, Slow Waves, and the EEG.- 14.1. Models of Field Potentials Resulting from Unit Activity.- 14.2. Models of the EEG.- 15 Models of Specific Neural Networks.- 15.1. Cerebral Cortex.- 15.2. Thalamus and Hippocampus: Brain Rhythms.- 15.3. Reticular Formation.- 15.4. The Retina and Lateral Inhibition.- IV. Conclusion.- 16 Neural Modeling: The State of the Art.- 16.1. The Stratification of Variables.- 16.2. Goals of Neural Network Modeling.- 16.3. Guidelines for Brain Modeling.- References.