University of Pennsylvania
EAS 244/CLST 344/INTG 344. Curiosity: Ancient and Modern Thinking about Thinking (Bassett, Struck)
The course examines two approaches to the still unanswered question of what happens when humans create knowledge. How should we describe the impulse, or set of impulses, that leads us to seek it? What is happening when we achieve it? And how do we describe the new state in which we find ourselves after we have it? We will study the work of contemporary physicists and cognitive scientists on these questions along side the approaches developed by the two most powerful thinkers from antiquity on the topic, Plato and Aristotle. The course will begin with Plato on knowledge, followed by an introduction to tools from network science and statistical physics that can be used to evaluate the structure of knowledge, the acquisition of knowledge, and the generation of new knowledge. The second portion of the course will focus on Aristotle’s thoughts on wonder, teleology in nature, gradients of the mind, and related topics, followed by an introduction to theories and tools from network neuroscience and cognitive science on the practice of curiosity. The goal of the course is to provide students with the conceptual tools to consider what curiosity is, what its value is, what its dangers are, how it can be quantified, and how to measure it in their own writing and that of historical thinkers. What makes a good idea? What drives innovation? How can we foster it?
BE566. Network Neuroscience (Bassett)
The human brain produces complex functions using a range of system components over varying temporal and spatial scales. These components are coupled together by heterogeneous interactions, forming an intricate information-processing network. In this course, we will cover the use of network science in understanding such large-scale and neuronal-level brain circuitry. The course will begin with a brief introduction to network science and associated tools for data analysis, mathematical modeling, and statistical inference. We will then examine the use of structural and functional brain networks extracted from non-invasive neuroimaging data (fMRI, MEG, MRI, DTI, DSI) in determining fundamental organizational principles of neuronal processes. We will review evidence for alterations in these network structures in psychiatric disease, neurological disorders, and brain injury. We will conclude with a section focused on the use of dynamic networks in understanding cognitive functions including learning and lexical processing, and emerging applications of network control theory for an understanding of cognition and clinical interventions. The goal of this course is to provide students with an intuition for the types of problems at the biological interface that network science can be used to address. To that end, we will place particular emphasis on: (i) network methods, (ii) data objects amenable to network methods, (iii) neurophysiological perturbations of such data, and (iv) current frontiers and open mathematical problems. Students will also learn how to peer review journal articles, and write reviewer reports. The will also learn how to peer review grant proposals and write reviewer reports. Lastly, they will learn to perform a network-based research project, and bring it from initial data through to a final publication-quality article in the style of Proc Natl Acad Sci.
ENM375. Fundamentals of biostatistics
The goal of this course is to equip bioengineering undergraduates with fundamental concepts in applied probability, exploratory data analysis and statistical inference. Students will learn statistical principles in the context of solving biomedical research problems. The purpose of this course is to provide students with skills to analyze and interpret small and large biological data set. Fundamentals in statistics will be taught through the use of homework problems, case studies and projects focused on computational analysis of biological data. Topics covered include: populations and samples; random variables; discrete and continuous probability distributions; exploratory data analysis; descriptive statistics (mean, standard deviation, median, variance, quantiles); confidence intervals; expectations; variances; central limit theorem; independence; hypothesis testing; fitting probability models; pvalues; goodness-of-fit tests; correlation coefficients; non-parametric tests; ANOVA; linear regression; bootstrapping; and maximum likelihood estimation.