Description:

The European Union (HBP) and United States (BRAIN) have announced plans to make significant investments in brain science over the next decade. Given the large amount of money on the table it makes sense to ask the question: “What’s holding up progress and how might we make the best use of the new funding?” Neuroscientists have by and large failed to take advantage of the exponential trend in computational power known as Moore’s Law. In this class, we investigate new approaches to scalable neuroscience that might enable systems neuroscience to exploit the accelerating returns from recent advances in sensing and computation.

Modern systems neuroscience is fundamentally multi-disciplinary, and most advances are based on collaborations that cut across disciplinary boundaries. Engineers in nanotechnology may not know a great deal about the biochemistry of neural signal transduction, but teamed with the appropriate experts in those fields, they are able to build nanoscale sensors for recording neuron action potentials. Computer scientists may not know the intricacies of developing Hodgkin-Huxley neuron models, but they routinely collaborate with neuroscientists who do understand such models to build large-scale neural circuits. This is how systems neuroscience at the best research labs works in the 21^st Century.

The course will be graded pass / no credit on the basis of class participation, a midterm white paper or business prospectus and a final technical report evaluating an appropriate technology selected in collaboration with the instructor. Examples of promising technologies include nanoscale networks, photoacoustic microscopy, high-intensity focused ultrasound and computer-vision-based analysis of micrographs. The emphasis will be on exploring new hybrid technologies, consulting with scientists and engineers in the relevant fields, and evaluating opportunities for investment. Technology-minded critical thinkers seriously interested in placing their bets and picking careers in related areas of business, technology, and science are welcome. Prerequisites: good math skills, mastery of high-school biology and physics.

Addendum: June 25, 2013: Eight students completed the class for a grade. An account of our class discussions can be found by following the "calendar" and "discussion" links at the top of this page. A jointly-authored report describing our findings entitled "On the Technology Prospects and Investment Opportunities for Scalable Neuroscience" is available in HTML and PDF formats.

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: Bharath Bhat

Email: bbhat [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 (30%)

- Project proposal due around midterm (20%)

- Project report due around finals week (50%)