April 29: Lecture 1: Course goals, setup, Brian present: everyone
Content: Jakob, Jan, Michael, Poornima
Jakob, Jan, Jan-Matthis
May 6: Lecture 2: Mechanistic models I May 13: Lecture 3: Mechanistic models II May 20: Lecture 4: Optimizing mechanistic models Jakob, Artur, Auguste
May 27: Lecture 5: Generalized linear models I June 3: Lecture 6: Generalized linear models II June 10: Lecture 7: Nonlinear encoding models Ruben, Joe, ??
June 17: Lecture 8: Acquisition of large imaging datasets. 2-photon imaging, lightsheet imaging, silicon probes. Behavior and stimuli. June 24: Lecture 9: Pre-processing data: image alignment, de-noising (understanding sources of noise), anatomical segmentation.
July 1: Lecture 10: Template matching, Functional segmentation, Activity extraction: matrix of cells x activity.
July 8: Lecture 11: Stimulus and behavior related activity. Multivariate linear regression. July 15: Lecture 12: Intro to high-dimensional spaces [JOE] July 22: Lecture 13: PCA, ICA, NNMF, phase space July 29: Lecture 14: Clustering and decoding (tentatively, or exam)