| LEARNING AND REPRSENTATION | | |
W 1/14 | 1. Introduction |
NIH Brain Facts (chapter 1)
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M 1/16 | 2. Neurons and Membranes |
F. Rosenblatt - Perceptron.
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M 1/21 | Martin Luther King Day (no class) | | |
W 1/23 | 3. Spikes and Cables | Trappenberg Ch 1.1-2.2 (C) | HW 1 out |
M 1/28 | 4. Synapse and Neural Net | Trappenberg Ch 3.1 | |
W 1/30 | 5. Polar Vortex (no class) |
McCulloch and Pitts (1943)
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M 2/4 | 6. Synaptic plasticity | Trappenberg Ch 4
Abbott and Nelson (2000)
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W 2/6 | 7. Hebbian Learning | Trappenberg Ch 4, HPK Ch 8
Oja (1982)
| HW 2 out, HW 1 in |
M 2/11 | 8. Features and Convolutions | HPK Ch 8 | |
W 2/13 | 9. Computaitonal Maps | HPK Ch 9 | |
M 2/18 | 10. Source Separation | | |
W 2/20 | 11. Hopfield Net | | HW 2 in, HW 3 out. |
M 2/25 | 12. Composition | | |
W 2/27 | 13. Multi-layer perceptron | | |
M 3/4 | 14. Midterm | | |
W 3/6 | 15. Deep convolutional neural network | | HW4 out. |
M 3/11 | Midterm grade, Spring break | | |
W 3/13 | Spring break | | |
| ASSOCIATION and INTERACTION | | |
M 3/18 | 16. Local Recurrent Circuits | | |
W 3/20 | 17. Bayesian Inference and Decoding | | HW 3 in |
M 3/25 | 18. Memories and imagination | | |
W 3/27 | 19. Semantic category development | | |
M 4/1 | 20. Gated Machines | | HW 4 in, HW 5 out |
W 4/2 | 21. Decision Processes | | |
M 4/8 | 22. Reinforcement Learning | | |
W 4/10 | 23. Predictive Learning | | |
F 4/13 | Spring Carnival | | |
M 4/15 | 24. Attention and Feedback Network | | |
W 4/17 | 25. Cell, Circuit and System ID | | HW5 in |
M 4/22 | 26. Reading Neural Codes | | |
W 4/24 | 27. Brain Machine Interface | | |
M 4/29 | 28. Neural Manifold Learning | | |
W 5/1 | 29. Review and Summary | | Term paper deadline |
F 5/3 | 30. 686 project presentation | | presentation deadline |
X 5/X | Final Exam | | |
R 5/16 | Final Grade due 4 p.m. for Graduates | | |
T 5/21 | Final Grade due 4 p.m. | | |