Date of Award
Doctor of Philosophy (PhD)
Statistics and Data Science
Recent years have witnessed a surge of problems lying at the intersection of statistics andneuroscience. In this thesis, we explore various statistical and computational problems that are inspired by neuroscience. This thesis consists of two main parts, each inspired by a different system in the brain. In the first part, we study problems related to the visual system. In Chapter 2, we investigate the problem of estimating the collision time of a looming object using a theoretical formulation based on statistical hypothesis testing. In Chapter 3, we build computational models for the compound eye of Drosophila, and analyze how the models recover features of actual visual loom-selective neurons. In the second part, we study problems related to the memory system. In Chapter 4, we consider approaches for accelerating and reducing memory requirements for reinforcement learning algorithms, with provable guarantees on the performance of the algorithms
Li, Zifan, "On Neuroscience-Inspired Statistical and Computational Problems" (2021). Yale Graduate School of Arts and Sciences Dissertations. 371.