 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 1.12.2 (C)  Matlab tutorial Wean 5201 4:305:30 
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   8:3011:30 a.m. 
R 5/14  Final Grade due 4 p.m. for Graduates   
T 5/19  Final Grade due 4 p.m.   