Current & Past Courses
Cognitive Neuroscience (NSCI/PSYC)
This lecture course will cover the fundamental and advanced principles underlying several cognitive functions from the perspectives of modern cognitive, systems, and computational neuroscience. The materials in cognitive neuroscience will be discussed in both humans and animal models of human cognition by going over general neurobiological principles followed by several key examples from research studies that have influentially shaped the field. Throughout this course, students will be familiarized with the neural mechanisms underlying cortical and subcortical control of cognition (e.g., decision-making, attention, executive control, emotion, social cognition, and memory), as well as various neuroscience techniques (neuroimaging, invasive and noninvasive electrophysiological recording, and neuropharmacological manipulations) and analytical methods. Overall, the course will provide a strong foundation in cognitive neuroscience informed by underlying neurobiological principles.
Systems Neuroscience (PSYC/NSCI)
This seminar course provides an overview of the fundamental principles governing the central nervous system. Topics include the anatomy of the central nervous system, the neural mechanisms underlying cortical and subcortical control of behavior, various neuroscience techniques, as well as implications for nervous system disorders. The class will discuss basic knowledge of the nervous system with the key experimental findings that led to new discoveries in brain function.
Foundations of Neuroscience: Biological bases of human behavior (PSYC/NSCI)
The purpose of this core course is to provide an understanding of the biological factors underlying human cognition and behavior. A particular emphasis will be placed on the mechanisms associated with individual differences in healthy functions (including emotion regulation, stress sensitivity, higher cognition, reward sensitivity, impulsivity, and social functions) and their relations with psychiatric and neurological disorders. Biological factors to be covered include genetic, neuroanatomical, neurophysiological, neurochemical, hormonal, and neuropsychological influences. Several of the initial sessions will be devoted to basic topics (e.g., neurons, neuronal signaling, brain systems) before we begin our discussion of the neural basis of behavior and cognition. We will also cover seminal work on animal models for mechanistic insights into the neurobiology of human behavior. We encourage graduate students with any neuroscience research interest to take this course. For the neuroscience- area students, this is a required course.
Foundations of Systems Neuroscience (INP)
The purpose of this core course for the INP is to provide foundational knowledge of systems neuroscience.
Statistics and Data Analysis in Neuroscience (INP/NBIO)
Sections on “Hypothesis testing” and “Model comparisons”
This course focuses on practical applications of various statistical models and tests commonly used in neuroscience research. It covers basic probability theory, hypothesis testing, and maximum likelihood estimation, as well as model comparison. The specific models and tests covered include ANOVA, regression, time series analyses, and dimension reduction techniques (e.g., PCA). Examples and homework will be given in MATLAB, which will be introduced at the beginning of the course. Previous experience in programming and basic statistics is desirable but not required.
Neurobiology of Cortical Systems (NBIO)
Section on “Prefrontal cortex and Social Cognition”
Survey course on the development, function and dysfunction of the cortex.
Computational Modeling & Analysis in Neuroscience (NSCI)
Section on “Behavioral Data Analysis: Visualization of Choice Data”
The aim of this course is to introduce students to state-of-the-art methods that are used for data analysis and computational modeling of behavior and neural activity. Classes will combine discussions of primary research papers with coding tutorials to facilitate focused, hands-on exploration of quantitative methods of interest. Topics will include modeling decision-making, model selection, time-frequency analysis of neural activity, and neural population models.