CS379C: Computational Models of the Neocortex

Spring 2017


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.

Location and Time:

TTh 4:30 - 5:50pm in the Hewlett building, room 103


Instructor: Thomas Dean

Email: tld [at] google [dot] com

Office hours: by appointment


Course Assistant: Amy Christensen

Email: amyjc [at] stanford [dot] com


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.


- Class participation including presentation (30%)

- Project proposal due around midterm (20%)

- Project report due around finals week (50%)


[1]   Fei Chen, Paul W. Tillberg, and Edward S. Boyden. Expansion microscopy. Science, 347:543-548, 2015.

[2]   Pawel Dlotko, Kathryn Hess, Ran Levi, Max Nolte, Michael Reimann, Martina Scolamiero, Katharine Turner, Eilif Muller, and Henry Markram. Topological analysis of the connectome of digital reconstructions of neural microcircuits. CoRR, arXiv:1601.01580, 2016.

[3]   B. N. Giepmans, S. R. Adams, M. H. Ellisman, and R. Y. Tsien. The fluorescent toolbox for assessing protein location and function. Science, 312:217-224, 2006.

[4]   D. H. Hubel and T. N Wiesel. Integrative action in the cat’s lateral geniculate body. Journal of Physiology, 155:385–398, 1961.

[5]   D. H. Hubel and T. N Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology, 160:106-154, 1962.

[6]   Saul Kato, Harris S. Kaplan, Tina Schrödel, Susanne Skora, Theodore H. Lindsay, Eviatar Yemini, Shawn Lockery, and Manuel Zimmer. Global brain dynamics embed the motor command sequence of caenorhabditis elegans. Cell, 163:656-669, 2015.

[7]   R. Clay Reid. From functional architecture to functional connectomics. Neuron, 75:209-217, 2012.

[8]   H. Sebastian Seung. Neuroscience: Towards functional connectomics. Nature, 471:170-172, 2011.

[9]   Xiaokun Shu, Varda Lev-Ram, Thomas J. Deerinck, Yingchuan Qi, Ericka B. Ramko, Michael W. Davidson, Yishi Jin, Mark H. Ellisman, and Roger Y. Tsien. A genetically encoded tag for correlated light and electron microscopy of intact cells, tissues, and organisms. PLoS Biology, 9:e1001041, 2011.

[10]   O. Sporns, G. Tononi, and R. Kötter. The human connectome: A structural description of the human brain. PLoS Computational Biology, 1:e42, 2005.