Introductory course to research in computational approaches to neuroscience at the University of Washington and affiliated institutions. Meets weekly for talks from research faculty across campus and beyond.
A supplement to the core Neurobiology laboratory course in cellular neurobiology. Introduces concepts of neuronal and network modeling and how these dynamics underlie coding. Co-taught with Eric Shea-Brown.
NBIO 450B: Journal club in quantitative neuroscience
Reading course in neuroscience papers, with a computational/quantitative bias. Led by graduate students from the Computational Neuroscience program.
Discussion course treating three levels of neural processing through discussion of original literature. Each unit starts with classic papers and closes with a contemporary paper inheriting from the classic work:
- sensory transduction in photoreceptors and the relationship between noise at the transduction stage and perception (Hecht, Schlaer and Pirenne, Baylor; led by Fred Rieke);
- single neuron dynamics and coding (Hodgkin and Huxley; led by Adrienne Fairhall)
- representations in visual cortex (Hubel and Wiesel; led by Anitha Pasupathy)
A survey of mathematical and data analysis methods with applications in neuroscience. Combination of lecture, matlab tutorials and student presentations of original literature. Co-taught with Fred Rieke and Wyeth Bair.
A graduate course in computational neuroscience loosely based on Dayan and Abbott, Theoretical Neuroscience. Topics include coding, decoding, information theory, biophysics of computation, networks, learning and memory. Co-taught with Rajesh Rao.
An introductory course based on Dayan and Abbott. Co-taught with Rajesh Rao.