Neuronal dynamics : from single neurons to networks and models of cognition
- , Cambridge, United Kingdom, Cambridge University Press 2014
- 577P:
Machine generated contents note: pt. ONE FOUNDATIONS OF NEURONAL DYNAMICS 1. Introduction: neurons and mathematics 1.1. Elements of neuronal systems 1.2. Elements of neuronal dynamics 1.3. Integrate-and-fire models 1.4. Limitations of the leaky integrate-and-fire model 1.5. What can we expect from integrate-and-fire models? 1.6. Summary 2. Ion channels and the Hodgkin Huxley model 2.1. Equilibrium potential 2.2. Hodgkin Huxley model 2.3. The zoo of ion channels 2.4. Summary 3. Dendrites and synapses 3.1. Synapses 3.2. Spatial structure: the dendritic tree 3.3. Spatial structure: axons 3.4.Compartmental models 3.5. Summary 4. Dimensionality reduction and phase plane analysis 4.1. Threshold effects 4.2. Reduction to two dimensions 4.3. Phase plane analysis 4.4. Type I and type II neuron models 4.5. Threshold and excitability 4.6. Separation of time scales and reduction to one dimension 4.7. Summary. Contents note continued: pt. TWO GENERALIZED INTEGRATE-AND-FIRE NEURONS 5. Nonlinear integrate-and-fire models 5.1. Thresholds in a nonlinear integrate-and-fire model 5.2. Exponential integrate-and-fire model 5.3. Quadratic integrate and fire 5.4. Summary 6. Adaptation and firing patterns 6.1. Adaptive exponential integrate-and-fire 6.2. Firing patterns 6.3. Biophysical origin of adaptation 6.4. Spike Response Model (SRM) 6.5. Summary 7. Variability of spike trains and neural codes 7.1. Spike-train variability 7.2. Mean firing rate 7.3. Interval distribution and coefficient of variation 7.4. Autocorrelation function and noise spectrum 7.5. Renewal statistics 7.6. The problem of neural coding 7.7. Summary 8. Noisy input models: barrage of spike arrivals 8.1. Noise input 8.2. Stochastic spike arrival 8.3. Subthreshold vs. superthreshold regime 8.4. Diffusion limit and Fokker Planck equation (*) 8.5. Summary. Contents note continued: 9. Noisy output: escape rate and soft threshold 9.1. Escape noise 9.2. Likelihood of a spike train 9.3. Renewal approximation of the Spike Response Model 9.4. From noisy inputs to escape noise 9.5. Summary 10. Estimating parameters of probabilistic neuron models 10.1. Parameter optimization in linear and nonlinear models 10.2. Statistical formulation of encoding models 10.3. Evaluating goodness-of-fit 10.4. Closed-loop stimulus design 10.5. Summary 11. Encoding and decoding with stochastic neuron models 11.1. Encoding models for intracellular recordings 11.2. Encoding models in systems neuroscience 11.3. Decoding 11.4. Summary pt. THREE NETWORKS OF NEURONS AND POPULATION ACTIVITY 12. Neuronal populations 12.1. Columnar organization 12.2. Identical neurons: a mathematical abstraction 12.3. Connectivity schemes 12.4. From microscopic to macroscopic 12.5. Summary. Contents note continued: 13. Continuity equation and the Fokker Planck approach 13.1. Continuity equation 13.2. Stochastic spike arrival 13.3. Fokker Planck equation 13.4.Networks of leaky integrate-and-fire neurons 13.5.Networks of nonlinear integrate-and-fire neurons 13.6. Neuronal adaptation and synaptic conductance 13.7. Summary 14. Quasi-renewal theory and the integral-equation approach 14.1. Population activity equations 14.2. Recurrent networks and interacting populations 14.3. Linear response to time-dependent input 14.4. Density equations vs. integral equations 14.5. Adaptation in population equations 14.6. Heterogeneity and finite size 14.7. Summary 15. Fast transients and rate models 15.1. How fast are population responses? 15.2. Fast transients vs. slow transients in models 15.3. Rate models 15.4. Summary pt. FOUR DYNAMICS OF COGNITION 16.Competing populations and decision making 16.1. Perceptual decision making. Contents note continued: 16.2.Competition through common inhibition 16.3. Dynamics of decision making 16.4. Alternative decision models 16.5. Human decisions, determinism, and free will 16.6. Summary 17. Memory and attractor dynamics 17.1. Associations and memory 17.2. Hopfield model 17.3. Memory networks with spiking neurons 17.4. Summary 18. Cortical field models for perception 18.1. Spatial continuum model 18.2. Input-driven regime and sensory cortex models 18.3. Bump attractors and spontaneous pattern formation 18.4. Summary 19. Synaptic plasticity and learning 19.1. Hebb rule and experiments 19.2. Models of Hebbian learning 19.3. Unsupervised learning 19.4. Reward-based learning 19.5. Summary 20. Outlook: dynamics in plastic networks 20.1. Reservoir computing 20.2. Oscillations: good or bad? 20.3. Helping patients 20.4. Summary
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience