Biological and Artificial Intelligence

Neuro 140 | Neuro 240

word cloud AI

Harvard Canvas Site [login required]


Notes and Slides

Reading materials 

Tutorial sessions


Homework, Project Timeline and Credits

List of projects

Meeting Times & Location

Frequently Asked Questions

Suggested Books

Academic Integrity Policy



First class: Tuesday January 23th, 2024. 3pm.   

Please fill in this survey before class starts

This is a seminar-style course which provides a foundational overview of key 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: Andrei Barbu, Richard Born, Boaz Barak, Jan Drugowitsch, Sam Gershman, Gabriel Kreiman, Cengiz Pehlevan, Haim Sompolinsky, Tomer Ullman, Will Xiao

Course Meeting Times and Schedule

Tuesdays 3:00 pm to 5:00 pm

> Additional tutorial sessions will be announced soon


Frequently asked questions

Location: Northwest Building B101

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.

• Kreiman G (2021). Biological and Computer Vision. Cambridge University Press.

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

• 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.

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

• Goodfellow I, Bengio Y, Courville A. (2016) Deep learning. MIT 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.

The final 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. Projects involving code and algorithms can use existing code from 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

Teaching Assistants

Morgan Talbot

Yervand Azatian

Office hours: 

Gabriel Kreiman:
Tuesdays, 11am-12pm ET
Zoon only:
Please click here to book appointment

Morgan Talbot:
Thursdays, 6-7pm ET.
Location: Northwest 243

Yervand Azatian:
Tuesdays 6-7 PM.
Location: Northwest 243