Neural Basic of Cognition/Machine Learning Thesis Proposal

Recently-developed technologies for monitoring activity in populations of neurons make it possible for the first time, in principle, to ask many basic questions in neuroscience. However, computational tools for analyzing newly available data need to be developed. The goal of this thesis is to contribute to this effort by focusing on two specific problems. In completed research, we used a point-process regression framework to provide a methodology for statistical assessment of the link between neural spike synchrony and network-wide oscillations. In simulations, we showed that our method can recover ground-truth relationships, and in two types of spike train data we illustrated the kinds of results the method can produce. The approach improves on methods in the literature, and may be adapted to many different experimental settings. We also consider the…


Link to Full Article: Neural Basic of Cognition/Machine Learning Thesis Proposal