An Invitation to the Study of Brain Networks, with Some Statistical Analysis of Thresholding Techniques

We provide a brief introduction to the nascent application of network theory to mesoscale networks in the human brain. Following an overview of the typical data-gathering, processing, and analysis methods employed in this field, we describe the process for inferring a graph from neural time series. A crucial step in the construction of a graph from time series is the thresholding of graph edges to ensure that the graphs represent physiological relationships rather than artifactual noise. We discuss the most popular currently employed methodologies and then introduce one of our own, based on the theory of random matrices. Finally, we provide a comparison of our random-matrix-theory thresholding approach with two dominant approaches on a data set of 1,000 real resting-state functional magnetic resonance imaging scans

Optimizing voxel scale graph theoretical analysis of fMRI-derived resting state functional connectivity

The analysis of neural functional connectivity from resting-state MRI data using tech­ niques derived form graph theoretical foundations has recently attracted a significant amount of research interest. The bulk of such work done to date focuses on relatively small graphs, derived by partitioning the brain into regions of interest. In this thesis we develop tools leveraging high-performance computing and meth­ ods for analyzing “whole brain” graphs in which we consider each grey-matter voxel in the brain to be an individual graph vertex. Based on 26 resting-state fMRI datasets we then empirically determine optimal sets of graph metrics for large graphs under varying assumptions followed by an investigation of the robustness of these metrics as assumptions are varied. We then demonstrate the application of our methods to the question of hierarchical organization in prefrontal cortex. We conclude by describing a technique for significantly reducing the size of our graphs, while losing as little useful information as possible

Abort, Retry, Fail? Why Computer Science is an Essential Part of Every Science Education

Scientists are often woefully unprepared for the rising use of computing in their work, according to research published in a recent edition of Nature [1]. In fact, survey results indicate that 45% of scientists spend more time developing software as part of their work than five years ago, and that 38% of all scientists spend at least one fifth of their time developing software. This is only natural, to assist in experimentation, interface with high tech equipment, or analyze a tremendous volume of measurements and results. The truly frightening part? Nearly all of what these scientists know of software development is self-taught, and they often lack even the base skills and background to realize just how bad they are at it. Formal Computer Science training was simply not a part of their educations. The results? Work is riddled with inaccuracies and errors, precious time and valuable resources are lost, and reputation in the scientific community dwindles as publications are retracted and proven wrong. The costs are staggering and only getting worse with time. The solution, fortunately, is fairly simple: Computer Science must be made an integral part of every science education. Delivering this solution, however, is not without its challenges. What instruction is required? How can it be tailored and made relevant to a variety of scientific disciplines? How can it be packaged and squeezed into already full curricula? How can this be done with already strained instructional resources? This presentation will delve into these and other issues, making the case for Computer Science as an essential part of science education. [1] Z. Merali. Computational Science: … Error … why scientific programming does not compute. Nature 467, 775-777 (2010). Available online at: http://www.nature.com/news/2010/101013/full/467775a.htm

Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance

Reinforcement learning (RL) problems often feature deceptive local optima, and learning methods that optimize purely for reward signal often fail to learn strategies for overcoming them. Deep neuroevolution and novelty search have been proposed as effective alternatives to gradient-based methods for learning RL policies directly from pixels. In this paper, we introduce and evaluate the use of novelty search over agent action sequences by string edit metric distance as a means for promoting innovation. We also introduce a method for stagnation detection and population resampling inspired by recent developments in the RL community that uses the same mechanisms as novelty search to promote and develop innovative policies. Our methods extend a state-of-the-art method for deep neuroevolution using a simple-yet-effective genetic algorithm (GA) designed to efficiently learn deep RL policy network weights. Experiments using four games from the Atari 2600 benchmark were conducted. Results provide further evidence that GAs are competitive with gradient-based algorithms for deep RL. Results also demonstrate that novelty search over action sequences is an effective source of selection pressure that can be integrated into existing evolutionary algorithms for deep RL.Comment: Submitted to GECCO 201

Floquet engineering of correlated tunneling in the Bose-Hubbard model with ultracold atoms

We report on the experimental implementation of tunable occupation-dependent tunneling in a Bose-Hubbard system of ultracold atoms via time-periodic modulation of the on-site interaction energy. The tunneling rate is inferred from a time-resolved measurement of the lattice site occupation after a quantum quench. We demonstrate coherent control of the tunneling dynamics in the correlated many-body system, including full suppression of tunneling as predicted within the framework of Floquet theory. We find that the tunneling rate explicitly depends on the atom number difference in neighboring lattice sites. Our results may open up ways to realize artificial gauge fields that feature density dependence with ultracold atoms.Comment: 8 pages, 9 figure