Biological and Artificial Intelligence

Neuro 140 | Neuro 240

word cloud AI

Harvard Canvas Site [login required]

Link to Survey


Notes and Slides

Reading Assignments

List of projects

Meeting Times & Location

Suggested Books

Academic Integrity Policy


NOTE: FIRST CLASS = January 29, 2019

This course provides a foundational overview of the fundamental ideas in computational neuroscience and the study of Biological Intelligence. At the same time, the course will connect the study of brains to the blossoming and rapid development of ideas in Artificial Intelligence. Topics covered include the biophysics of computation, neural networks, machine learning, Bayesian models, theory of learning, deep convolutional networks, generative adversarial networks, neural coding, control and dynamics of neural activity, applications to brain-machine interfaces, connectomics, among others. Lectures will be taught by leading Harvard experts in the field.

Faculty include: Barbu, Blum, Boix, Drugowitsch, Gershman, Kreiman, Mahadevan, Mathis, Pehlevan, Rakhlin, Samuel, Sompolinsky, Ullman

Course Meeting Times and Schedule
Tuesdays 3:00 pm to 4:15 pm

Thursdays 3:00 pm to 4:15 pm

Location: Emerson 108 [Google MAP]
(Note new location!)

Suggested Books:

There won’t be an official book for the class. Here are some interesting books that touch upon some of the topics covered in class. The class will not follow any of these books.

• Ullman S (1996) High-level vision. MIT Press.

• Wandell BA (1995) Foundations of vision. Sunderland Sinauer Associates.

• Chalupa LM and Werner JS (editors) (2003). The Visual Neurosciences. MIT Press.

• Ripley. Pattern recognition and neural networks (1996). Cambridge University Press.

• Rao, Olshausen and Lewicki (eds) (2002). Probabilistic models of the brain. MIT Press.

• Koch C (2005) The quest for consciousness. Roberts & Company Publishers.

• Dayan and Abbott (2002). Theoretical Neuroscience. MIT Press.

• Horn BKP. (1986) Robot Vision. MIT Press.

• Kriegeskorte N and Kreiman G. (2011) Understanding visual population codes. MIT Press.

• Davies ER. (2005). Machine Vision, Third Edition: Theory, Algorithms, Practicalities (Signal Processing and its Applications). Elsevier.

• Sutton RS, Barto AG. (2018) Reinforcement Learning: An Introduction. MIT Press. • Vapnik, V. (1998). The Nature of Statistical Learning Theory. Springer.

• Tegmark, M (2017) Life 3.0: Being Human in the Age of Artificial Intelligence. Random House.

• Poole D, Mackworth, A. (2017) Artificial Intelligence: foundations of computational agents, 2nd edition, Cambridge University Press.

• Poggio TA, Anselmi F. Visual Cortex and Deep Networks (2016). Learning Invariant Representations. MIT Press.

Academic Integrity Policy

Collaboration Policy Statement

Discussion with other students and with people outside the class is permitted throughout the course. Students can also utilize any relevant material from the library or the web. Students must adequately cite any material that they use.

Each student must work on his/her own project. No two projects can be identical. There can be no group projects. All work should be entirely the student's own work. The use of textbooks, books, articles, and web resources is encouraged.

If a student writes an essay on a given topic as their project, this write-up has to be exclusively the work of the student. If material is reported from other sources, it should be reported as a quote and cited.

If a student works on a project that involves codes and algorithms, they can use existing code in public repositories. Any such code should be adequately cited. All code used in any models or simulations should be turned in accompanying the final report.

Contact Information

Course coordinator and Teaching Assistant:
Nimrod Shaham

Teaching Assistant
Colin Conwell

Teaching Assistant
Kasper Vilken

Gabriel Kreiman