計算神經科學
Computational Neuroscience
學期
109-1
學分
3
學分
當期課號
A952
永久課號
B2021
開課單位
神經科學研究所
授課教師
陳俊仲
校區
陽明
類別
選修
上課時間表
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 計算神經科學 YL839 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course introduction, basic neurobiology, basic programming in python |
| 第 2 週 | Neural activities, firing rate and spike train statistics |
| 第 3 週 | Tuning curves, receptive fields, stimulus-response correlations |
| 第 4 週 | Spike-trigger average, principal component analysis, basic linear algebra |
| 第 5 週 | Decoding neural responses, discrimination, likelihood and probabilistic inference |
| 第 6 週 | Mutual information, Entropy, basic information theory |
| 第 7 週 | Electrophysiology of neurons, spiking-neuron models |
| 第 8 週 | Electrophysiology of neurons, spiking-neuron models |
| 第 9 週 | Synaptic transmissions, coupling of neural activities, computer simulations |
| 第 10 週 | Levels of neural modeling, computer representations, basic data structure |
| 第 11 週 | Assorted network models, simulations of neural networks |
| 第 12 週 | 全校運動會停課 |
| 第 13 週 | Synaptic plasticity, plasticity rules and network stability |
| 第 14 週 | Unsupervised learning, Hebbian learning, spike-timing dependent plasticity |
| 第 15 週 | Supervised learning, basic artificial neural networks |
| 第 16 週 | Behavior conditioning, reinforcement learning, Q-learning |
| 第 17 週 | Classification, generative models, self-organizing map |
| 第 18 週 | Neural functions, predictive dynamics, information filtering, decision making |