15-386/686 Neural Computation

Carnegie Mellon University

Spring 2020

Course Description

Neural Computation is an area of interdisciplinary study that seeks to understand how the brain learns and computes to achieve intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities -- such as perception, language, motor control, decision making and learning -- by building artificial systems and computational models with the same capabilities. This course explores computational issues at multiple levels, from individual neurons to circuits and systems, with a view to bridging brain science and machine learning. It will cover basic models of neurons and circuits, computational models of learning, memories and inference, and quantitative approaches to neural system analysis in real and artifical systems. Concrete examples will be drawn from the visual system and the motor system, with emphasis on relating current deep learning research and the brain research, from hierarchical computation, attention, recurrent neural networks, to reinforcement learning. Students will learn to perform quantitative analysis as well as computational experiments using Matlab and deep learning platforms. No prior background in biology or machine learning is assumed. Prerequisites: 15-100, 21-120 or permission of instructor. 21-241 preferred but not required.

Course Information

Instructors Office (Office hours) Email (Phone)
Tai Sing Lee (Professor) MI 115. Friday TBA tai@cnbc.cmu.edu (412-268-1060)
Darby Losey (TA) Wean Hall 6423. Monday 4:30-5:30 loseydm@cmu.edu
Geyang Zhang (TA) Wean Hall 5312. Tuesday 4:30-5:30. geyangz@andrew.cmu.edu

Recommended Supplementary Textbook

Classroom Etiquette

386 Grading Scheme

Evaluation% of Grade
6 Assignments 70
Class participation/attendance/quizzes 10
Midterm 10
Final Exam 10
Optional term project (Replacement of 1 HM or Exam) 10
  • Grading scheme: A: > 88 B: > 75. C: > 65 percent.
  • 686 Grading Scheme

    Evaluation% of Grade
    Assignments 70
    Class participation/attendance/quizzes 10
    Midterm 10
    Final Exam 10
    Option 1. Weekly Journal Club (reading / presentations) 25
    Option 2. Term project 25
    Option 3. Term project and journal club replace 25 assigment points (~2 assignments)
  • Total Score: 125. 686 students can choose one of the above three options for the 25 points.
  • Grading scheme: A: > 88 B: > 75. C: > 65 percent.
  • Assignments

    Term Project

    Examinations

    Late Policy

    Syllabus

    Date Lecture Topic Relevant Readings Assignments
      LEARNING AND REPRSENTATION    
    M 1/13 1. Introduction NIH Brain Facts (chapter 1)  
    W 1/15 2. Neurons and Membranes McCulloch and Pitts (1943) HW 1 out
    M 1/20 Martin Luther King Day (no class)    
    W 1/22 3. Spikes and Cables Trappenberg Ch 1.1-2.2 (C) Matlab tutorial Wean 5201 4:30-5:30
    M 1/27 4. Synapse and Neural Net Trappenberg Ch 3.1  
    W 1/29 5. Neuron models and Perceptron F. Rosenblatt - Perceptron. HW 2 out, HW 1 in
    M 2/3 6. Synaptic plasticity Trappenberg Ch 4 Abbott and Nelson (2000)  
    W 2/5 7. Hebbian Learning Trappenberg Ch 4, HPK Ch 8 Oja (1982)  
    M 2/10 8. Features and Convolutions HPK Ch 8  
    W 2/12 9. Computaitonal Maps HPK Ch 9 Kohonen (1982) HW 3 out. HW 2 in;
    M 2/17 10. Source Separation Foldiak (1990) Olshausen and Field (1997) (2004)  
    W 2/19 11. Belief Net Hinton and Salahutdinov (2006)  
    M 2/24 12. Belief Net Hinton and Salahutdinov (2006)
    W 2/26 13. Review    
    M 3/2 14. Midterm    
      ASSOCIATION and INTERACTION    
    W 3/4 15. Recurrent and Attractor network Hopfield and Tank (1986) HW 3 in. HW 4 out
    M 3/9 Midterm grade, Spring break    
    W 3/11 Spring break    
    M 3/16 16. Zoom Introduction No class  
    W 3/18 17.Recurrent Circuits Marr and Poggio (1976) Samonds et al. (2013)  
    M 3/23 18. Markov Bayesian Network Lee (1995) Kersten and Yuille (2003)  
    W 3/25 19. Probabilistic Bayesian Inference Weiss et al. (2002). Ma et al. (2006)  
    M 3/30 20. Neural network Fukushima (1988), Krizhevsky et al. (2012) HW 4 in. HW 5 out
    W 4/1 21. Convolutional Neural Networks Zeiler and Fergus (2013) LeCun, Bengio and Hinton (2015)  
    M 4/6 22. Deep Network and the Brain Yamins and DiCarlo (2016) Lillicrap et al. (2016)  
    W 4/8 23. Biological Plausible Learning Arrout et al. (2019), Guerguiev et al. (2017)  
    M 4/13 24. Hierarchical Feedback Mumford (1992) Rao and Ballard (1998) Lee and Mumford (2003)  
    W 4/15 25. Kalman Filter Welch and Bishop (2001) Rhudy et al. (2017) HW 5 in. HW 6 out.
    M 4/20 26. Motor System and BCI Sheahan et al (2016), Sadtler et al (2014), Oby et al (2018)  
    W 4/22 27. Predictive Learning Lotter et al (2016), Colah (2015) Rao (2015)  
    M 4/27 28. Reinforcement Learning Niv (2009), Montague et al. (1996)  
    W 4/29 29. Reinforcement Learning Gadagkar et al. (2016) HW 6 in
    F 5/1 30. Project Presentation   journal club time
    F 5/8 31. Final Exam   8:30-11:30 a.m.
    R 5/14 Final Grade due 4 p.m. for Graduates    
    T 5/19 Final Grade due 4 p.m.    

    Journal Club and Relevant Reading List

  • Every Friday 1:30-3:00, except 3/13 Spring Break, and 4/17 Spring Carnival. Total 13 weeks. Minimal attendance: 10 times (10 points). 20 points for 3-4 Presentations.
  • The papers below are reading choices of last year (see www.cnbc.cmu.edu/~tai/nc19.html). New papers will be added as we will explore some new topics, such as brain computer interface, mind readng, emotion and consciouness.

    Week 1 (1/24): Logical computation in Neurons

    Week 2 (1/31): Advances in Neural Circuits

    Week 3 (2/8): Computational models of Neural Circuits

    Week 4 (2/17): Sparse Coding and Reinforcement Learning

    Week 5 (2/24): Biological Plausible Deep Learning Algorithms

    Week 6 (3/16): Neural Basis of Bayesian Inference

    Week 7 (3/23): Bayesian Inference and Inverse Rational Control

    Week 8 (3/30) : Curiosity and Learning

    Week 9: (4/10) Reinforcement Learning and Song Birds

    Week 10: (4/17) Why do we have emotion?

    Week 11: (4/24) Why does consicouness do for us and animals?

    Week 12: (5/1) Glia -- the invisible essential workers


    Questions or comments: contact Tai Sing Lee
    Last modified: spring 2020, Tai Sing Lee