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Stochastic Methods in Neuroscience

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Table of Contents

PREFACE ; NOMENCLATURE ; 1. A brief introduction to some basic stochastic processes ; 2. Markov chain models of ion channels and calcium release sites ; 3. Stochastic dynamic bifurcations and excitability ; 4. Neural coherence and stochastic resonance ; 5. Noisy oscillators ; 6. The role of variablity in populations of spiking neuons ; 7. Population density methods in large-scale neural network modelling ; 8. A population density model of the driven LGN/PGN ; 9. Syanptic "noise": experiments, computatioal consequences and methods to analyze experimental data ; 10. Statistical models of spike trains ; 11. Stochastic simulations of neurons, axons, and action potentials ; 12. Numerical simulations of SDEs and SPDEs from neural systems using SDELAB

About the Author

Carlo Laing obtained his PhD in applied mathematics from the University of Cambridge. After post-doctoral positions in the UK, USA and Canada, he joined Massey University in Auckland, New Zealand, where he is currently a senior lecturer. His interests include nonlinear dynamical systems, particularly as applied in computational neuroscience. Gabriel Lord obtained his PhD from the University of Bath, UK. After a post-doctoral position at the University of Bristol and working for a time in industry he joined Heriot-Watt University in Edinburgh, UK. His research interests are in applied computational analysis, stochastic numerics and applications from computational neuroscience.

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