| Part 1: Neurons and Synapses | | |
M 2/1 | 1. Introduction and Overview |
NIH Brain Facts (chapter 1)
| |
W 2/3 | 2. Neurons and Membranes |
Trappenberg Ch 1.1-2.2
| HW 1 out |
F 2/5 | Recitation: Matlab tutorial | | |
M 2/8 | 3. Spikes and Cables | Trappenberg Ch 2 (C) | Matlab tutorial Wean 5201 4:30-5:30 |
W 2/10 | 4. Synapse and Dendrites | Trappenberg Ch 3.1, 3.3 | |
F 2/12 | Recitation: Review for Problem Set 1 | | |
M 2/15 | 5. Synaptic plasticity | Trappenberg Ch 4
Abbott and Nelson (2000)
| |
W 2/17 | 6. Hebbian Learning | Trappenberg Ch 4, HPK Ch 8
Oja (1982)
| HW 2 out, HW 1 in |
F 2/19 | Recitation: Review Part 1 | | |
| Part 2: Representation and Computation | | |
M 2/22 | 7. Computation and Logical Units |
Trappenberg 3.1,3.5
F. Rosenblatt - Perceptron.
McCulloch and Pitts (1943)
| |
W 2/24 | 8. Biological Visual Systems | Trappenberg 5.1.
Van Essen et al (1992)
Fellman and Van Essen ( 1991)
| |
F 2/26 | Recitation: Review Problem Set 2 | | |
M 3/1 | 9. Sparse Coding |
Foldiak (1990)
Olshausen and Field (1997)
(2004)
| |
W 3/3 | 10. Deep Belief Net |
Trappenberg Ch 10.3
Hinton and Salahutdinov (2006)
| HW 3 out. HW 2 in; |
F 3/5 | Recitation: Review for Problem set 3 | | |
W 3/8 | 11. Computational Maps | HPK Ch 9. Trappenberg 7.1-7.2
Kohonen (1982)
| |
W 3/10 | 12. Markov Network |
Marr and Poggio (1976)
Samonds et al. (2013)
Wang et al. (2018)
| |
F 3/12 | Recitation: Review Part 2 | | |
M 3/15 | Midterm | | |
| PART 3: Neural Networks | | |
W 3/17 | 13. Attractor network and Memory | Trappenberg Ch 8. Ch 9.4
Hopfield and Tank (1986)
| |
F 3/19 | No Recitation, Mid-semester break | | |
M 3/22 | 14. Causal inference (Model-selection) | | Mid-semester grade due |
W 3/24 | 15. Neural network (MLP) |
Trappenberg Ch 6, 10.1.
Fukushima (1988),
Krizhevsky et al. (2012)
| HW3 in. HW4 out. |
F 3/26 | Recitation: Review Problem set 4 | | |
M 3/29 | 16. Convolutional Neural Networks |
Zeiler and Fergus (2013)
LeCun, Bengio and Hinton (2015)
| |
W 3/31 | 17. Deep Network and the Brain |
Yamins and DiCarlo (2016)
Maheswaranathan et al. (2018)
Lillicrap et al. (2016)
| |
F 4/2 | Recitation: Review Part 3 | | |
M 4/5 | Easter Monday. No Class |
| |
| PART 4: PREDICTION AND FEEDBACK | | |
W 4/7 | 18. Biological Plausible Learning |
Arrout et al. (2019),
Guerguiev et al. (2017)
| HW4 due. HW5 out. |
F 4/9 | Recitation: Review Problem set 5 | | |
M 4/12 | 19. Hierarchical Inference |
Mumford (1992)
Rao and Ballard (1998)
Lee and Mumford (2003)
| |
W 4/14 | 20. Attention and Self-Attention |
Trappenberg Ch 10.
Vaswani et al. (2017)
Lindsay (2020)
Knudsen (2007)
| |
F 4/16 | Carnival No recitation | | |
M 4/19 | 21. Prediction and Surprise |
Trappenberg Ch 10.
Lotter et al (2016),
Colah (2015)
Rao (2015)
| |
W 4/21 | 22. Probabilistic Inference |
Weiss et al. (2002).
Ma et al. (2006)
Kersten and Yuille (2003)
| HW 5 in, HW 6 out. |
F 4/23 | Recitation: Review Problem set 6 and Journal Club | | |
M 4/26 | 23. Inference Mechanisms |
Orban et al. (2016).
Shivkumar et al. (2019)
| |
W 4/28 | 24. Reinforcement Learning |
Trappenberg. Ch 9.
Niv (2009),
Montague et al. (1996)
| |
F 4/30 | Journal Club | | |
M 5/3 | 25. Curiosity and Imagination |
Gruber et al. (2014)
Kidd and Hayden (2015).
| |
W 5/5 | 26. Emotion and Consciousness |
Koch (2018)
van Hateren (2019)
| HW 6 in |
F 5/7 | Recitation: Review of the Course | | |
W 5/12 | Term paper and everything due 2:00 pm.. | | |
M 5/17 | Final Exam Start | | |
R 5/20 | Final Grade due 4 p.m. for Graduates | | |
T 5/25 | Final Grade due 4 p.m. | | |