Arbib: CS564 "Brain Theory and AI" SyllabusFall 20071
/ CSCI 564 (NEUR 535): Fall 2008Brain Theory and Artificial Intelligence
Tu Th 11:00am-12:20pm
Instructor: Prof. Michael A. Arbib; HNB-03, (213)740-9220, . (Office hours: 1-2 pm Tuesdays, HNB 03.)
TA: Jinyong Lee. Office Hours: to be announced, HNB 10.
Brains have proved highly successful in integrating perception, planning, memory and action in guiding creatures that interact with a complex world. The course has two overlapping aims: “To understand the workings of our own brains” and “To explore the implications of brain function for developing exotic, highly distributed adaptive embodied computing systems.” As we move to distributed computation, sensor networks, embedded systems, and robots interacting with humans in complex ways, we will discover Brain Operating Principles (BOPs) that will not only illuminate our understanding of ourselves but will also guide us in the development of new brain-style adaptive, distributed embedded computing technologies.
The course will introduce you to the basic facts about the brain, teach you how to model the brain conceptually and how to implement those models in our Neural Simulation Language (NSL), and how to keep track of BOPs and brain models in our Brain Operation Database (BODB).
Course Requirements:
One “mid-term” will cover the entire contents of the lectures and required readings up to that time. The final will emphasize, but not be restricted to, material covered after the mid-term.
Each student will be required to prepare a four-part project to get an overall feel for the architecture of a largish brain model, understand how models are related to empirical data, and think through the details of at least one important subsystem. Joint work on Parts 3 and 4 of the Project is allowed but not required.
Prerequisites:
Graduate standing; ability to program in Java, C++ or MatLab or willingness to learn to program in one of these systems. Basic background in neuroscience will be supplied, but students with experience in this area are still invited to join the course to gain an understanding of the computational approach to the brain.
Neuroscience students less skilled in computer programming will still study MatLab and the NSL homework, but may either (a) negotiate a project that involves analysis of a neural system without computer implementation, or (b) conduct joint work on Parts 3 and 4 of the Project taking responsibility for literature review andsystem design rather than programming.
Texts:
[NSL Book]: A. Weitzenfeld, M.A. Arbib and A. Alexander, 2002, NSL Neural Simulation Language, MIT Press (A draft version is available at
[HBTNN] Selected articles from M.A. Arbib, Ed., 2003, The Handbook of Brain Theory and Neural Networks, MIT Press, Second Edition. (The Handbook is available as one of the reference works on-line at the Cognet website of The MIT Press. This can be reached from USC machines by going to
Other articles will be placed on the class Website including extracts from
[TMB2] M.A. Arbib, 1989, The Metaphorical Brain 2: Neural Networks and Beyond, Wiley-Interscience.
Grades: Homework: 20%; Mid-term: 20%; Final 20%; Project: 40% (5% for Part 1; 10% for Part 2; 5% for Part 3; 20% Part 4)
Syllabus
Date / Topic / Readings- 8/26
- 8/28
HBTNN: Part I, Sections I.1 and I.2; Part III, Single Cell Models
- 9/2
- 9/4
HBTNN 2e: Feature Analysis
- 9/9
Anon Plangprasopchok, Nantana Tinroongroj, and Michael A. Arbib, User’s Manual for the Brain Operation Database.
Supplementary Reading:
HBTNN: Schema Theory ; Multiagent systems
TMB2: Section 5.1 (more on frogs) and 5.4 (brief look at language)
HBTNN: Visuomotor Coordination in Frog and Toad; Hybrid Symbolic/Connectionist Systems
- 9/11
HBTNN: Part I, Sections I.1 and I.2; Part III, Single Cell Models
- 9/16
- 9/18
- 9/23
HBTNN: Collicular Visuomotor Transformations for Saccades
- 9/25
Supplementary Reading:NSL Book: Chapter 14 – The Modular Design of the Oculomotor System in Monkeys
[Even though it uses Java-NSL, not MatLab-NSL, this will help you prepare for the lecture on 10/23: Practical Introduction to MatLab-NSL 3: Basal Ganglia: Learning Associations and Sequences.]
- 9/30
[Include overview of later lectures on RL and backprop.] / Required Readings:TMB2: Section 3.4. HBTNN: Associative Networks: Perceptrons, Adalines, and Back-Propagation.
Supplementary Readings: HBTNN: Competitive Learning; Hebbian Synaptic Plasticity. NSL Book: Chapter 12: The Associative Search Network: Landmark Learning and Hill Climbing
- 10/2
Adaptive networks 2: Reinforcement learning; Conditional motor learning / Required Reading: HBTNN: Reinforcement Learning; Reinforcement Learning in Motor Control
- 10/7
Supplementary Reading: HBTNN: Backpropagation
- 10/9
- 10/14
- 10/16
- 10/21
- 10/23
Supplementary Reading from HBTNN: Decoding Population Codes; Grasping Movements: Visuomotor Transformations.
- 10/28
- 10/30
Reprint: Dominey, P.F., Arbib, M.A., and Joseph, J.-P., 1995, A Model of Corticostriatal Plasticity for Learning Associations and Sequences, J. Cog. Neurosci., 7:311-336
- 11/4
Optional: HBTNN: Dynamics and bifurcations in neural nets (Ermentrout).
- 11/6
- 11/11
- 11/13
HBTNN: Competitive Queuing
- 11/18
- 11/20
- 11/25
- 12/2
- 12/4
Project 4 due / Readings from the previous lecture continued.
Final Exam