Phase Response Curves in Neuroscience: Theory, Experiment, and Analysis [Hardcover]

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.Inspired by response to a workshop at the 2008 OCNS meeting, this book tracks 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.

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 phaslc2