Description:
This year's class focuses on inferring computational models from neural-recording data. Lectures, invited speakers and projects all emphasize functional rather than structural inference. A major goal of computational neuroscience is to produce predictive mesoscale theories of biological computation that bridge the gap between the cells and the behaviors of complex organisms thereby explaining how the former give rise to the latter. Until recently there was little hope of formulating testable theories of this sort. However, with new technologies for recording the activity of thousands, even millions of neurons simultaneously, it is now feasible to observe neural activity at a scale and resolution that opens the possibility of inferring such theories directly from data. We will arrange access to large functional datasets along with tools for working with such datasets and suggestions for modeling methods and machine learning technologies for performing inference. Grading is based on class participation and final projects. Team projects are encouraged. Prerequisites include algorithms, programming, basic statistics and probability theory plus some familiarity with machine learning.
Addendum: October 14, 2017: A technical report entitled "Inferring Mesoscale Models of Neural Computation" describing a methodology for learning mesoscale models from aligned functional and structural recordings of whole organisms and derived in part from the lectures and class discussions held during the 2016 and 2017 instantiations of CS379C is available on arXiv as PDF.
Location and Time:
TTh 4:30 - 5:50pm in the Hewlett building, room 103
Staff:
Instructor: Thomas Dean
Email: tld [at] google [dot] com
Office hours: by appointment
Course Assistant: Amy Christensen
Email: amyjc [at] stanford [dot] com
Textbooks:
There are no required textbooks for this course but
you are expected to do a lot of reading on your own
and these three texts are good to have around for
reference. I’ve yet to meet anyone who has
read all three cover to cover but over the years
I’ve probably read most of the chapters in one
edition or the other and often found them relevant.
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- Neuroscience: Exploring the Brain (Third Edition), Bear, Connors and Paradiso.
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- The Cognitive Neurosciences (Third Edition), Gazzaniga.
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- Principles of Neural Science (Fourth Edition), Kandel, Schwartz and Jessell.
Grading:
- Class participation including presentation (30%)
- Project proposal due around midterm (20%)
- Project report due around finals week (50%)
References: