Models of the stochastic activity of neurones

1. Stochastic fluctuations in membrane potential. - 1.1 Thermal or Johnson-Nyquist Noise. - 1.2 Shot Noise. - 1.3 Flicker Noise. - 1.4 Conductance fluctuations. - 1.5 References. - 2. Quantal Fluctuations in Generator Potential. - 2.1 Psychophysical Evidence for Quantal Sensitivity in Vision. - 2.2 Some Neural Machines. - 2.3 Fluctuations in the Limulus Eccentric Cell Generator Potential. - 2.4 References. - 3. Models of Action Potential Initiation. - 3.1 The Logical Neurone. - 3.2 The Perfect Integrator Model. - 3.3 The Leaky Integrator Model. - 3.4 Two-Time Constant Models. - 3.5 The Hodgkin-Huxley Equations. - 3.6 Reduced Hodgkin-Huxley Equations. - 3.7 References. - 4. Fluctuations in Excitability. - 4.1 Single Time Constant Models with Fluctuating Threshold. - 4.2 Two-Time Constant Models with Fluctuating Threshold. - 4.3 Fluctuations in Hodgkin-Huxley variables. - 4.4 Models with Time-varying Threshold. - 4.5 References. - 5. Statistical Properties of a Spike Train. - 5.1 Stationarity. - 5.2 Interval Densities and Distributions. - 5.3 Spectral Densities of Point Processes. - 5.4 Input-Output Relations. - 5.5 References. - 6. Random Walk Models. - 6.1 The Random Walk Model. - 6.2 The Probability of First Passage. - 6.3 The Interval Probability Density. - 6.4 A Diffusion Approximation. - 6.5 Introduction of a Reflecting Barrier. - 6.6 Generalization of the Random Walk as a Birth and Death Process. - 6.7 Birth and Death Process Model of the Leaky Integrator. - 6.8 More Complex Random Walks. - 6.9 Some Comments on the Random Walk Models. - 6.10 References. - 7. Diffusion Models. - 7.1 The Input Process. - 7.2 The Wiener Process. - 7.3 The Ornstein-Uhlenbeck Process. - 7.4 The Diffusion Equations. - 7.5 The First Passage Time Distribution. - 7.6 The SIPIT Model. - 7.7 The SILIT Model. - 7.8 The Inverse Approach to Diffusion Models. - 7.9 References. - 8. Superposition Models. - 8.1 Some General Properties of Superposed Processes. - 8.2 Superposition of Regular Generator Trains. - 8.4 Some Applications of Superposition Theory. - 8.5 References. - 9. Collision Models. - 9.1 The Mechanism of Collision. - 9.2 The Geometry of Collision. - 9.3 Mutual Interaction Between Two Non-adjacent Generator Sites. - 9.4 The Biology of Collision. - 9.5 References. - 10. Gating and Selective Interaction Models. - 10.1 Compound Exponential Distributions and Gating Models. - 10.2 Selective Interaction Models. - 10.3 Models with Sub-threshold Interactions. - 10.4 Neural Micro-nets with Reciprocal Inhibition. - 10.5 References. - 11. Models of Synaptic Transmission. - 11.1 The neuro-muscular junction. - 11.2 Spontaneous miniature end-plate potentials. - 11.3 Presynaptic depolarization enhanced release. - 11.4 Models of facilitation and depression. - 11.5 Transmitter induced fluctuations in conductance. - 11.6 Dynamics of signal transfer across a synapse. - 11.7 References. - 12. Models of the stochastic activity of neural aggregates. - 12.1 Nets of logical neurones. - 12.2 Networks continuous in time and space: a field approach. - 12.3 Temporal behaviour of nets of excitatory and inhibitory neurones. - 12.4 Spatially distributed excitatory and inhibitory subpopulations. - 12.5 Some qualitative speculations. - 12.6 References. - 13. Information transmission by model neurones. - 13.1 The axon as a channel. - 13.2 Information preservation in a neural chain. - 13.3 Signal detection. - 13.4 Tranmission of steady signals using a rate code. - 13.5 Information transmission of periodic signals. - 13.6 Transmission of stochastic, time-varying signals. - 13.7 Transinformations of different codes. - 13.8 References.