CS379C: Computational Models of the Neocortex

[2017 WEBPAGES UNDER CONSRUCTION]

Description:


Cell neurobiologists know a good deal about individual neurons, the signaling pathways between pairs of neurons, and the dynamics of small networks of neurons. Cognitive neuroscientists, on the other hand, can tell you a lot about the behavior of organisms and the parts of their nervous systems that appear to be responsible for governing that behavior. There is, however, relatively little known about how microscale molecular processes implement the computations that underlie cognition and ultimately cause observable macroscale behavior.


A major goal of computational neuroscience is to produce predictive mesoscale theories of biological computation that bridge the gap between the cells and 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.


In this year's class, we continue our investigation of functional connectomics, the study of how the structure of neural circuits and the activity of their constituent neurons perform the functions necessary for cognition and control. In the last year alone, hundreds of papers have been published describing new methods for inferring function from recorded structure and activity. A number of the authors of those papers will participate in class to describe their methods and share their models and data. Students taking the class for credit can apply these models or those of their own devising to learn models from neural recordings provided by participating labs.


The prerequisites are basic high-school biology, good math skills, and familiarity with machine learning. Some background in computer vision and signal processing will be important for projects in structural connectomics. Familiarity with modern artificial neural network technologies is a plus for projects in functional connectomics.


Location and Time:


[NOTE NEW TIME & LOCATION FOR 2017]

TTh 4:30-5:50, Room 103, Hewlett Building




Staff:


Instructor: Thomas Dean

Email: tld [at] google [dot] com

Office hours: by appointment

 

Course Assistant: TBD

Email: TBD




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.

  • - Neuroscience: Exploring the Brain (Third Edition), Bear, Connors and Paradiso.

  • - The Cognitive Neurosciences (Third Edition), Gazzaniga.

  • - 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%)