Neuroscience

In this course we learn about the mind by looking at (1) multiple levels of analysis, from neurons to social systems, (2) multiple methodologies used in research, and (3) how multiple types of explanation (mechanism, function, ontogeny, phylogeny) shed light on each other. Using these three course objectives, we will build up a framework for understanding the full range of topics in cognitive science, and how they relate to other disciplines both within the social sciences (e.g., political science and economics) and beyond them (e.g., biology and computer science).

“Nothing in biology makes sense, except in the light of evolution” – T. Dobzhansky. From the relationships among species in a forest to the interactions of molecules in a cell, evolution is ultimately responsible. One might be tempted to view Dobzhansky’s quote as indicating that evolution is one key that unlocks the complexity of biology. That view is supported by statements such as “survival of the fittest,” which oversimplify the complexity of evolution. Instead, Evolution at Multiple Scales views evolution as the elaborate set of interconnected concepts it is. Although Darwin published On the Origin of Species over 150 years ago, evolutionary biology continues to be augmented, as new discoveries are driven by new technologies. By evaluating evolutionary concepts in a broad range of biological scenarios, students deepen their understanding of evolution itself, shedding light on the diversity of life it has produced. This course qualifies as part of the Interdisciplinary Minor in Sustainability because it addresses biodiversity. NS112 focuses on the evolutionary processes producing biodiversity, and also addresses the benefits of biodiversity to humans, the consequences of biodiversity loss, and strategies to maintain it.

Explore how the brain gives rise to the mind through the lens of cognitive neuroscience. Learn about the anatomy, physiology, and chemistry of the brain and consider the role of this physical substrate in neural computation. This course introduces the methodological foundations of cognitive neuroscience and their application to analyzing specific mental processes and events, with links to related fields such as genetics and computational neuroscience. In addition, it provides a framework for understanding cognitive disorders, mental thriving and human development, which supports student engagement in public policy or social ventures.Topics include the evolution of the brain, consciousness, vision, motor control, speech, memory, executive function, developmental psychology, and disorders of the brain such as depression, schizophrenia, Alzheimer's disease and autism spectrum disorder.

Investigate how biological traits are determined. Examine how genetic and environmental influences are translated through cellular and developmental mechanisms to determine the properties of cells, organisms, and species. Apply concepts and approaches from genetics, developmental biology, and computational biology to fundamental questions, including how to determine disease risk and how gene expression is finely coordinated and tuned. Explore the ethical and societal implications of genetics research and applications, including the impact on human health and behavior. NOTE: In addition to the listed prerequisites, the following courses are recommended prior to taking this course: CS130

This course covers applications of research methods and data analysis. We use primary research literature related to seven case studies to examine different forms of quantitative, qualitative, and mixed methods research, and to understand the ways in which these techniques help address specific research questions. Primary texts for the course cover the broader context of research designs and the use of the R programming language for data analysis and visualization. Secondary texts relate to the case studies. Students facilitate class sessions, choosing and assigning to the rest of the class their own readings and original framing of a research approach to an assigned topic. Note: NS125/SS125 may be substituted for a tutorial in NS/SS and can count like a cross-listed tutorial for double majors in SS, NS, or CS (any pairwise combination). In addition to the listed prerequisites, the following courses are recommended prior to taking this course: CS130

The course focuses on the application of predictive and causal statistical inference for decision making across a wide range of scenarios and contexts. The first part of the course focuses on parametric and non-parametric predictive modeling (regression, cross-validation, bootstrapping, random forests, etc.). The second part of the course focuses on causal inference in randomized control trials and observational studies (statistical matching, synthetic control methods, encouragement design/instrument variables, regression discontinuity design, etc.). Technical aspects of the course focus on computational approaches and real-world challenges, drawing cases from the life sciences, public policy and political science, education, and business. This course also emphasizes the importance of being able to articulate one’s findings effectively and tailor methodology and policy/decision-relevant recommendations for different audiences. Note: CS130 may be substituted for a tutorial in CS/NS/SS and can count like a cross-listed tutorial for double majors in SS, NS, or CS (any pairwise combination). This course was previously CS112.

Methods are explored to interpolate data, solve linear and non-linear systems of equations, and model dynamical systems with the use of ordinary and partial differential equations. Additionally, Fourier Analysis is applied to model and process signals. Numerical implementations of the mathematical methods are developed using MATLAB or Octave. NOTE: Students may request to take CS113 in the same semester, as a corequisite.

Learn how to apply advanced modeling techniques to analyze and predict the behavior of social, physical and economic systems. You will learn from specific examples applied to portfolio management, traffic flow management, and analyzing social networks. The course covers three modeling frameworks — cellular automata for modeling interactions on grids of cells, networks for more general interactions between nodes in a graph, and Monte Carlo simulations showing how we can use simulation to generate random numbers and how we can use random numbers to drive simulations of complex phenomena. The course covers the theoretical (mathematical) and practical (implementation) aspects of each of the three frameworks.