Date: Friday, February 16, 2018
Location: 1084 East Hall (4:10 PM to 5:00 PM)
Title: Phase Response Curves and Computational Neuroscience: an introduction and an analysis of the effects of PRCs on EI network dynamics
Abstract: The Phase Response Curve (PRC) is an experimentally obtainable measure used to articulate certain properties of a neuron's excitability profile. However, the concept of the PRC also arises from the mathematical study of dynamical systems, particularly coupled oscillators. Given the interdisciplinary development of this measure, it is an extremely useful tool for computational neuroscientists seeking to understand the dynamics of complex neural networks, particularly those that tend to exhibit neural synchrony.
In this talk, I will introduce the PRC from both a mathematical and neuroscientific standpoint and describe how this measure is obtained in both settings. I will then illustrate the usefulness of this measure in the context of recently published research in which I studied the how properties of the PRC and changing network topologies interact to differentially affect the tendency for the classic EI neural network to exhibit synchronous oscillations. This work is of interest to the neuroscientific community given that changes to the PRC of the type studied here can be achieved by modulation by the ubiquitous neuromodulator Acetylcholine, while also of interest to mathematicians interested in the dynamics of complex networks of coupled oscillators, in this case represented by neurons.
Files:
Speaker: Scott Rich
Institution: University of Michigan
Event Organizer: Audra McMillan amcm@umich.edu
