Mathematical Tools for Neuroscience
NEU 314, Fall 2016
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time: Tu/Th 10:00-11:00 |
location: PNI A02 |
instructor: Jonathan
Pillow
| AIs:Alex Riordan, Nick Roy, Anqi Wu.
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Princeton University |
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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 |
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Lecture Schedule |
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Date | Topic |
Reading / Handouts |
Slides (pdf) |
HW |
| Th 9.15 |
Course introduction and announcements |
syllabus.pdf
| lec01.pdf
| hw0.ipynb
hw0Answers.ipynb
| Tu 9.20 |
Intro to linear algebra: vectors, norm, and inner
product
| IntroPython.pdf |
lec02.pdf
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| Th 9.22 |
linear projection & matrices
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lec03.pdf
| hw1.ipynb
(due 10/4)
| Tu 9.27 |
vector spaces
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lec04.pdf
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Th 9.29 |
trichromactic color vision
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lec05.pdf
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Tu 10.4 |
trichromacy, part II
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lec06.pdf
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Th 10.9 |
matrix mult, outer product, row/column space
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lec07.pdf
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Tu 10.11 |
linear systems, SVD
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Th 10.13 |
pseudoinverse, condition number
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Tu 10.18 |
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Th 10.20 |
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Tu 10.25 |
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| Th 10.27 |
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Tu 11.1 |
fall break
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Th 11.3 |
fall break
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Tu 11.8 |
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Th 11.10 |
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Tu 11.15 |
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Th 11.17 |
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Tu 11.22 |
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Th 11.24 |
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Tu 11.29 |
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Th 12.1 |
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Tu 12.6 |
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Th 12.8 |
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Tu 12.13 |
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Th 12.15 |
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page maintained by Jonathan Pillow |
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