First day of classes: Pitt January 7, 2019; CMU January 14, 2019
Note: students in the CNBC graduate training program automatically have instructor permission to attend any of these core courses, but cross-registration procedures may apply.
Students are expected to complete all of the core courses by the end of their third year. Students are encouraged to take advantage of elective courses when they are offered.
This introductory course in computational neuroscience is intended for a broad range of CNBC students, with backgrounds that may be either technical (math, engineering, statistics, etc.) or non-technical (biology, neuroscience, etc.). The course is co-taught by Brent Doiron and Rob Kass. Pitt students should register in MATH 3375; CMU students may register in 36-759. The two instructors settled on “statistical models” as a unifying theme for the many kinds of models discussed, ranging from those that describe the physiology of neurons to those that describe human behavior. Statistical ideas have been part of neurophysiology since the first probabilistic descriptions of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago; they have been part of experimental psychology even longer. In broad stroke, this course will examine a few of the most important methods and claims that have come from applying statistical thinking to the brain. However, some of the topics involve tools typically taught in statistics courses, while other topics involve tools taught in math courses. Even at an intuitive level, a single course can not provide a comprehensive view of computational neuroscience; the field is too broad. Instead, by studying a series of examples, many of them very influential, students will come away with a sense of the way that computational methods contribute to contemporary understanding of neuroscience.
This course provides an overview of Parallel-Distributed-Processing/neural-network models of perception, memory, language, knowledge representation, and learning. The course consists of lectures describing the theory behind the models as well as their implementation, and their application to specific empirical domains. Students get hands-on experience developing and running simulation models.
Modern neuroscience is an interdisciplinary field that seeks to understand the function of the brain and nervous system. This course provides a comprehensive survey of systems neuroscience, a rapidly growing scientific field that seeks to link the structure and function of brain circuitry to perception and behavior. This course will explore brain systems through a combination of classical, Nobel prize-winning data and cutting edge primary literature. Topics will include sensory systems, motor function, animal behavior and human behavior in health and disease. Lectures will provide fundamental information as well as a detailed understanding of experimental designs that enabled discoveries. Finally, students will learn to interpret and critique the diverse and multimodal data that drives systems neuroscience. This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.
This course is a component of the introductory graduate sequence designed to provide an overview of neuroscience. This course provides an introduction to the structure of the mammalian nervous system and to the functional organization of sensory systems, motor systems, regulatory systems, and systems involved in higher brain functions. It is taught primarily in a lecture format with some laboratory work.