Description:

This is the third time I've taught this course and a great deal has changed in the field of computational neuroscience since 2006. With the recent press releases about large projects to simulate the brain, there seems to be a widespread popular opinion that we now know enough to have some chance of succeeding in the endeavor in the near term. Most experts I know disagree with this viewpoint, and, among other areas where our current knowledge is inadequate, often cite our meager understanding of neuronal gene expression and the incredible diversity of signaling mechanisms and highly dynamic character of neural communication across multiple scales. I believe that success will come sooner or later but it will likely hinge on progress in a number of critical enabling technologies that are just beginning to mature and no doubt a few that have not yet been invented. These technologies include the ability to stimulate and record simultaneously from large populations of neurons, to determine gene expression levels within individual neurons and across ensembles, and to read off the wiring diagram of neural networks right down to the molecular level.

Working at Google, I am acutely aware of the advantages and the challenges of working at large scales, as well as the importance of educating and motivating the next generation of scientists and engineers to work at unprecedented scales of engineering. This year students in CS379C will have the opportunity to interact with some of the most innovative scientists and engineers working in systems neuroscience today. We will study their methods and hear directly from them about the challenges they face, some of which the students can actually help out with now. We will look at state-of-the-art computing technologies, see if they are up to the considerable computational challenges facing systems neuroscience, and, if not, what can be done to influence the relevant technology roadmaps. Finally, we will apply what we've learned to projects that exercise our understanding of the key problems or contribute directly to solving them.

Students will be graded on their presentation, class participation, and a project to be determined in collaboration with the teaching staff. Projects will include replicating and evaluating existing computational models and implementing novel models that extend or combine the features of existing ones. Small interdisciplinary group projects are encouraged. The projects will be graded on the basis of an initial proposal which will be due around midterm and a final report and demonstration due during the exam period. There will be no traditional midterm or final exams.

Location and Time:

MW, 4:15-5:30pm, Gates 100

 

Staff:

Instructor: Thomas Dean

Email: tld [at] google [dot] com

Office hours: by appointment

 

Course Assistant: Rohan Kamath

Email: rdkamath [at] stanford [dot] edu

 

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 them cover to cover but over the years, I’ve probably read most of the chapters in one edition or the other and found them consistently useful. A copy of each book will be put on the reference desk should you want to read a selection, and, in the case of the latter two, you can also often find preprint versions of individual chapters on the web pages of the contributing authors:

• - 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 (20%)

- Project proposal due around midterm (20%)

- Project documentation and demonstration (60%)