CS379C: Computational Models of the Neocortex

Spring 2019

Description:


This class focuses on building agents that achieve human-level performance in specialized technical domains and are adept at collaborating with humans using natural language. We draw upon research in cognitive and systems neuroscience to take advantage of what is known about how humans communicate and solve problems in order to design advanced artificial neural network architectures. Introductory lecture material is available here, a sample of final project suggestions here and last year's calendar of invited talks here.

In class discussions, we consider the design of a personal assistant that works with a software engineer in the role of an apprentice. The programmer's apprentice considered here is a novice programmer but possesses a set of innate skills in the form of powerful programming tools that constitute an integral part of its neural architecture and serve to ground its use of language. The assistant application allows us to explore the role of language in solving challenging problems and communicating declarative and procedural knowledge.



Time and Location:


TTh 4:30 - 5:50pm, Building 380 Room 380C




Staff:


Instructor: Thomas Dean

Email: tld [at] google [dot] com

Office hours: by appointment

Instructor: Rishabh Singh

Email: rising [at] google [dot] com

Office hours: by appointment

 

Course Assistant: Shreya Shankar

Email: shreya1 [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 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%)