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Would you mind updating the description to be as follows:Foundational scientific issues and philosophical perspectives related to advances in the study of brain and behavior. Implications of new insights from the neurosciences for medical therapy, social policy, and broader conceptions of human nature including consciousness, free will, personal identity, and moral responsibility. Topics include ethical issues related to genetic screening and editing, desire and addiction, criminal behavior, the biology of sexuality, fetal pain, aging and neurodegenerative disease, brain-computer interfaces, and neural enhancement and the human future. May be taken for 2 units without a research paper. Undergraduates must enroll in NBIO101. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit. Application required: https://forms.gle/8Kzg2v2izmK7tVgX6

Prerequisite: consent of instructor. (Staff)

Would you mind updating the description to be as follows:Foundational scientific issues and philosophical perspectives related to advances in the study of brain and behavior. Implications of new insights from the neurosciences for medical therapy, social policy, and broader conceptions of human nature including consciousness, free will, personal identity, and moral responsibility. Topics include ethical issues related to genetic screening and editing, desire and addiction, criminal behavior, the biology of sexuality, fetal pain, aging and neurodegenerative disease, brain-computer interfaces, and neural enhancement and the human future. May be taken for 2 units without a research paper. Undergraduates must enroll in NBIO101. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit. Application required: https://forms.gle/8Kzg2v2izmK7tVgX6

With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. This course will study statistical machine learning methods for analyzing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. We will develop the theory behind these models and algorithms and then apply them to real datasets in the homeworks and final project. Prerequisites: STATS 202 or CS 229

Prerequisite: consent of instructor.