Teaching

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Introduction to Neuroscience

In this course, students learn how the structure and function of the central and peripheral nervous systems support mind and behavior. Topics include neuroanatomy, developmental neurobiology, neurophysiology, neuropharmacology, and neuropsychiatry. The course is designed for prospective majors and nonmajors who are interested in exploring a field in which biology and psychology merge, and to which many other disciplines (e.g., chemistry, philosophy, anthropology, computer science) have contributed.
Winter 2023 Syllabus
Winter 2022 Syllabus
Winter 2019 Syllabus

Computational Neuroscience

In this course, students examine formal models of brain function to determine how neurons give rise to thought. Examining real datasets, students explore how the brain encodes and represents information at cellular, network, and systems scales, and they discuss ideas about why the brain is organized as it is. Specific topics include spike statistics, reverse correlation and linear models of encoding, dimensionality reduction, cortical oscillations, neural networks, and algorithms for learning and memory. All assignments and most class work emphasizes computer programming in Python though no programming background is assumed or expected.
Open textbook produced by class: Version 2.0
Fall 2022 Syllabus
Fall 2020 Syllabus
Fall 2019 Syllabus
Fall 2018 Syllabus

Neuroethics

As our ability to measure, predict, and manipulate brain function progresses, so too does our need to grapple with the societal consequences of neuroscientific discovery. This course invites critical examination of the ethics surrounding real-world neuroscience applications in private and public sectors. With topics that include psychopharmacology and cognitive liberty, neuroimaging for lie detection, weaponization of neurotechnology, and neuroprivacy in an era of data mining, students engage two overarching questions: How does the practice of neuroscience simultaneously mirror and mould social attitudes and policy-making agendas? What does it mean to be a responsible consumer and/or producer of neuroscientific knowledge?
Winter 2023 Syllabus
Fall 2021 Syllabus
Winter 2020 Syllabus
Fall 2018 Syllabus

Neural Codes: The Language of Thought

Although a central tenet of neuroscience is that information about the world in encoded in the patterns of neural firing, it is increasingly acknowledged that our assumptions about these patterns make qualitatively different predictions about neural function. This course examines major hypotheses related to information coding by individual neurons and populations of neurons. Specific themes include rate coding versus time-based codes, sparse versus dense codes, and the relationship between brain responses and the statistics of their inputs. Students examine biological data and artificial models to assess how various encoding schemes might produce skillful behavioral responses.
Winter 2022 Syllabus
Winter 2019 Syllabus

Neural Networks

Biological intelligence is characterized by selecting, processing, and storing information while flexibly adapting to changing conditions. How might biology inspire “smart” algorithms? This course explores the fundamental principles of artificial neural networks (ANNs). Students begin with modeling learning in a single computational unit (McCulloch-Pitts neuron), and then examine how many simple units can collectively give rise to complex behaviors. They examine both supervised networks that learn a predetermined input-output relationship, and unsupervised networks that learn “suspicious coincidences” from the input data. They implement neural networks with Python (previous experience is helpful but not necessary). Prerequisite(s), which may be taken concurrently: NS/PY 160. Recommended background: Experience with Python programming (such as from DCS 109) would be helpful, but is not strictly required.
Fall 2021 Syllabus

Statistical Methods

This course provides a hands-on introduction to modern statistical methods for brain and behavioral data. Topics include descriptive statistics, introductory probability theory, and statistical inference using both frequentist (hypothesis tests and confidence intervals) and Bayesian approaches, regression, prediction, analyses of variance, and resampling techniques including bootstraping. Particular emphasis is placed on design choices for reproducible research. Lectures are interactive, using the R programming language. No prior programming experience is required.
Fall 2019 Syllabus

Capstone Seminar in Human Cognitive Neuroscience

This seminar focuses on the end-to-end process of scientific discovery using the tools of human cognitive neuroscience. Students work in groups to uncover an open empirical question in the areas of perception, attention, or memory, then design and execute an experiment aimed at answering this question using electroencephalography or eye tracking in human subjects. Students gain experience in modern data analysis techniques including multivariate pattern analysis, time-frequency analysis, image processing, and representational similarity analysis.
Fall 2020 Syllabus
Winter 2018 Syllabus