Methods in Computational NeuroscienceNEU 394P (57580)PSY 394U (44190)Spring 2010SEA 2.224T, TH 2:00-3:30Instructor: Jonathan Pillow, Assistant Professor of Psychology & Neurobiology !Office: SEA 4.104Office hours: by appointmente-mail: [email protected] website: http://pillowlab.cps.utexas.edu/teaching/CompNeuro10/ Textbook: Theoretical Neuroscience. Dayan & Abbott. MIT Press (2001)I. Course description: This course aims to introduce students to computational methods for modeling the behavior of neurons and neural populations, with a particular emphasis on information processing in the nervous system. A tentative list of topics includes: neural encoding and decoding, receptive field characterization, point processes & spike train statistics, simplified spiking models, neural population coding, information theory, efficient coding / redundancy reduction, generative models (ICA), representational learning (Hopfield nets, Boltzmann machines, deep belief networks), and Bayesian modeling of human behavior. The course is aimed at students from quantitative backgrounds (engineering, math, physics, computer science, neuroscience, psychology) who are interested in the theory and analysis of neural systems.II. Course Format: This is a lecture course, but students will be expected to keep up with course readings, homework, and to participate actively in class. It is my belief that the best way to learn theoretical concepts is by actively putting them to use. The majority of the grade will therefore be based on homework assignments, which will involve both programming (implementation of algorithms) and paper-and-pencil problem solving. Students will also complete a final project, alone or in collaboration with another student, and will make a 20-minute presentation on this project during the final portion of the course.III. Course Requirements: 1. Homework. Homework will be assigned approximately every 2 weeks. Assignments should be handed in at the beginning of class on the assigned due date. Late assignments will not be accepted. Students are encouraged to work together on homework problems, but are expected to write computer code in groups of not more than two. 2. Attendance.Attendance is mandatory. Please notify the instructor in advance of any unavoidable absences. Please be on time, and do not leave early unless you have obtained prior permission from the instructor.3. Participation.Class participation will count toward 10% of the final grade. Some class periods will be devoted to the discussion of homework problem sets, and students will be expected to contribute and be prepared to explain answers on the board during these meetings.4. Course ProjectsAll students will complete a course project on some topic related to computational neuroscience, which will count for 30% of the final grade. Students may work alone or in groups of two. I will provide a list of suggested topics / project ideas, but students are also free to come up with their own proposals based on course readings, problem sets, or their own interests. Example projects might involve the implementation and extension of a method discussed in class, or the application one of these methods to neural data. I will meet at least twice with all groups to approve project proposals, and to monitor progress and offer guidance / suggestions. The last several lectures will be devoted to student presentations of their projects. This presentation will count for roughly half the project grade.IV. Grading. Grades will be based on:(a) Homework (60%)(b) Final Project (30%)(c) Class Participation (10%)V. Topic overview: This is a rough outline of topics I would like to cover, but the emphasis and timing will change as a function of the level of interest and enthusiasm of students and the instructor. Refer to the course website for an updated list of topics and assigned readings.• Introductory lecture: introduction to computational neuroscience• Intro/Review of mathematical concepts: probability, linear algebra, linear regression• TN Chapter 1: neural encoding and decoding• TN Chapter 2: reverse correlation and visual receptive fields• Special topics lecture on spike-triggered covariance• Special topics lecture on generalized linear models (GLMs) of neural spike trains• TN Chapter 3: population coding and decoding• TN Chapter 4: Information Theory• TN Chapter 5 (part 1): Integrate & fire; estimation of intracellular models from data• TN Chapter 7: Network models• TN Chapter 9: Representational Learning• Special topics lecture: ICA & Generative models• Special topics lecture: Bayesian models of human sensory & motor functionV. Class schedule: Several classes will need to be rescheduled due to travel and other conflicts. ! Thurs, Feb 25 & Tues, Mar 2. (Cosyne meeting)! Other dates: TBA All changes will be announced via email and posted on the website. I will do everything possible to find a consensus time for makeups.VII. Academic IntegrityEach student in this course is expected to abide by the University of Texas Honor Code. University of Texas Honor CodeThe core values of The University of Texas at Austin are learning, discovery, freedom, leadership, individual opportunity, and responsibility. Each member of the university is expected to uphold these values through integrity, honesty, trust, fairness, and respect toward peers and community.VIII. Other University Notices and Policies Use of E-mail for Official Correspondence to StudentsAll students should become familiar with the University's official e-mail student notification policy. It is the student's responsibility to keep the University informed as to changes in his or her e-mail address. Students are expected to check e-mail on a frequent and regular basis in order to stay current with University-related communications, recognizing that certain communications may be time-critical. It is recommended that e-mail be checked daily, but at a minimum, twice per week. The complete text of this policy and instructions for updating your e-mail address are available at http://www.utexas.edu/its/policies/emailnotify.html.Documented Disability StatementAny student with a documented disability who requires academic accommodations should contact Services for Students with Disabilities (SSD) at (512) 471-6259 (voice) or 1-866-329-3986 (video phone). Faculty are not required to provide accommodations without an official
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