Mathematical Tools for Neuroscience

NEU 314, Fall 2016
time: Tu/Th 10:00-11:00
location: PNI A02
instructor: Jonathan Pillow
AIs:Alex Riordan, Nick Roy, Anqi Wu.
Princeton University
prerequisites: Good working knowledge of calculus and high school math (e.g., a friendly relationship with log, exp, trig functions). Programming experience is helpful but not required.
brief description: This course aims to provide a comprehensive introduction to the mathematical and computational tools used for analyzing neural systems and neural data. The course will introduce students to topics in linear algebra, differential equations, and probability & statistics, with a heavy emphasis on applications to neurobiology. The course will seek to give students both a good intuitive understanding and a practical mastery of various mathematical and computational methods, and equip them with programming and data visualization skills that are increasingly important to scientific inquiry in general, and neuroscience in particular. Students will learn to program in Python, and homework problem sets will focus heavily on programming.
syllabus: pdf
piazza: course page
Lecture Schedule

DateTopic Reading / Handouts Slides (pdf) HW
Th 9.15 Course introduction and announcements syllabus.pdf lec01.pdf hw0.ipynb
Tu 9.20 Intro to linear algebra: vectors, norm, and inner product IntroPython.pdf lec02.pdf
Th 9.22 linear projection & matrices lec03.pdf hw1.ipynb
(due 10/4)
Tu 9.27 vector spaces lec04.pdf
Th 9.29 trichromactic color vision lec05.pdf
Tu 10.4 trichromacy, part II lec06.pdf
Th 10.9 matrix mult, outer product, row/column space lec07.pdf
Tu 10.11 linear systems, SVD
Th 10.13 pseudoinverse, condition number
Tu 10.18
Th 10.20
Tu 10.25
Th 10.27
Tu 11.1 fall break
Th 11.3 fall break
Tu 11.8
Th 11.10
Tu 11.15
Th 11.17
Tu 11.22
Th 11.24
Tu 11.29
Th 12.1
Tu 12.6
Th 12.8
Tu 12.13
Th 12.15

page maintained by Jonathan Pillow