Phase response curves in neuroscience : theory, experiment, and analysis
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Bibliographic Information
Phase response curves in neuroscience : theory, experiment, and analysis
（Springer series in computational neuroscience / series editors, Alain Destexhe, Romain Brette, v. 6）
Springer, c2012
- : pbk.
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book will track advances in the application of phase response (PR) analysis to the study of electrically excitable cells, focusing on applications of PR analysis in the computational neurosciences. This proposal was motivated by discussions with colleagues at the 2007 meeting of the Organization for Computational Neuroscience (OCNS) and further motivated by the success of a workshop at the 2008 OCNS meeting this past July. At that meeting the editors hosted a workshop entitled A dialogue for theoreticians and experimentalists: What is phase response analysis, and what can it tell us about neurons and networks? Invited speakers used mathematical, modeling, and experimental results to illustrate how phase response analysis has been used to reveal or describe neuronal and neuronal population dynamics. This was the most well-attended workshop of the meeting and was standing room only.
Table of Contents
- Preface.- Part 1
- Foundations of Phase Response Analysis.- Introduction to Part 1.- Chapter 1. The theory of weakly coupled oscillators.- Chapter 2. Phase resetting neural oscillators: Topological theory versus the real world.- Chapter 3. A theoretical framework for the dynamics of multiple intrinsic oscillators in single neurons.- Chapter 4. History of the application of the phase resetting curve to neurons coupled in a pulsatile manner.- Part 2
- Estimation of Phase Response Curves.- Introduction to Part 2.- Chapter 5. Experimentally estimating phase response curves of neurons: Theoretical and practical issues.- Chapter 6. A geometric approach to phase resetting estimation based on mapping temporal to geometric phase.- Chapter 7. PRC estimation with varying width intervals.- Chapter 8. Bayesian approach to estimating phase response curves.- Part 3
- Cellular Mechanisms of Neuronal Phase Response Properties.- Introduction to Part 3.- Chapter 9. Phase response curves to measure ion channel effects on neurons.- Chapter 10. Cellular mechanisms underlying spike-time reliability and stochastic synchronization: Insights and predictions from the phase-response curve.- Chapter 11. Recovery of stimuli encoded with a Hodgkin-Huxley neuron using conditional PRCs.- Chapter 12. Cholinergic neuromodulation controls PRC type in cortical pyramidal neurons.- Chapter 13. Continuum of type I somatic to type II dendritic PRCs
- Simulatingin vitro and in vivo phase response properties of a morphologically reconstructed Globus Pallidus neuron model.- Part 4
- Prediction of Network Activity with Phase Response Curves.- Introduction to Part4.- Chapter 14. Understanding activity in electrically coupled networks using PRCs and the theory of weakly coupled oscillators.- Chapter 15. The role of intrinsic cell properties in synchrony of neurons interacting via electrical synapses.- Chapter 16. A PRC description of how inhibitory feedback promotes oscillation stability.- Chapter 17. Existence and stability criteria for phase locked modes in ring networks using phase resetting curves and spike time resetting curves.- Chapter 18. Phase resetting curve analysis of global synchrony, the splay mode and clustering in N neuron all to all pulse-coupled networks.- Chapter 19. Effects of the frequency dependence of phase response curves on network synchronization.- Chapter 20. Phase-resetting analysis of gamma-frequency synchronization of cortical fast-spiking interneurons using synaptic-like conductance injection
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