| 01 |
9/18 |
Course Introduction: Models & modeling |
| 02 |
9/25 |
Behavioral Modeling (1/2): System dynamics |
| 03 |
9/26 |
Behavioral Modeling (2/2): Agent-based modeling |
| 04 |
10/9 |
National Day (Bridge Holiday) |
| 05 |
10/16 |
Computational Cognitive Science (1/2): Basics |
| 06 |
10/23 |
Computational Cognitive Science (2/2): Advanced |
| 07 |
10/30 |
Computational Cognitive Neuroscience (1/4): Modeling principles & canonical neural computation |
| 08 |
11/6 |
Computational Cognitive Neuroscience (2/4): Overview of learning & memory |
| 09 |
11/13 |
Computational Cognitive Neuroscience (3/4): Local learning & memory |
| 10 |
11/20 |
Computational Cognitive Neuroscience (4/4): Global learing & memory |
| 11 |
11/27 |
Deep-learning Neural Networks (1/5): Fully-Connected Multilayer Perceptron (MLP) |
| 12 |
12/4 |
Deep-learning Neural Networks (2/5): Convolutional Neural Network (CNN) |
| 13 |
12/11 |
Deep-learning Neural Networks (3/5): Recurrent Neural Networks (RNN) |
| 14 |
12/18 |
Deep-learning Neural Networks (4/5): Deep Reinforcement Learning (RL) |
| 15 |
12/25 |
Deep-learning Neural Networks (5/5): Advanced issues |
| 16 |
1/1 |
National Day |
| 17 |
1/8 |
Computational Neuroscience (1/2): 1 spiking neuron |
| 18 |
1/15 |
Computational Neuroscience (2/2): N spiking neurons |