計算神經科學
Computational Neuroscience
| 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
工具基礎: 以python程式語言編程 線性代數 微分方程 信息論 神經資訊的表示法: 峰波序列和激發率 尖峰觸發平均值和相關性 調變曲線和感受場 分辨性和推論法 互信息和熵 神經迴路的建模: 峰波神經元 突觸傳輸 神經網絡 細節化和抽象化 功能性: 自調式動力學和可塑性 信息過濾和預測 決定法 神經網絡的學習方式 Basics on tools: Programming in python Linear algebra Differential equations Information theory Neural representations: Spike trains and firing rates Spike-trigger average and correlation Tuning curve and receptive fields Discrimination and inference Mutual information and entropy Modeling neural circuits: Spiking neurons Synaptic transmissions Neuronal networks Details and abstractions Functions: Adaptive dynamics and plasticity Information filtering and prediction Decision making Learning in neural networks
本課程將涵蓋神經科學問題的計算方法的基礎方面,包括:分析建模、數值計算、數據處理、可視化和功能應用。 This course will cover fundamental aspects of computational approaches to neuroscience problems, including: analytical modeling, numerical calculations, data processing, visualization, and functional applications.
大約每周一次的作業 偶爾的隨堂測驗及指定閱讀 期末考 Homeworks will be assigned about week Occasional quizzes will base on reading assignments Final examine
| 週次 | 主題 |
|---|---|
| 第 1 週 | 課程介紹,神經生物學基礎,python基礎編程 Course introduction, basic neurobiology, basic programming in python |
| 第 2 週 | 神經活動、激發率和峰波序列統計 Neural activities, firing rate and spike train statistics |
| 第 3 週 | 調變曲線、感受場、刺激-反應相關性 Tuning curves, receptive fields, stimulus-response correlations |
| 第 4 週 | 峰波觸發平均、主成分分析、線性代數基礎 Spike-trigger average, principal component analysis, basic linear algebra |
| 第 5 週 | 解碼神經反應、分辨法、可能性和概率性推論 Decoding neural responses, discrimination, likelihood and probabilistic inference |
| 第 6 週 | 互信息、熵、基礎信息論 Mutual information, Entropy, basic information theory |
| 第 7 週 | 神經元電生理學,峰波神經元模型 Electrophysiology of neurons, spiking-neuron models |
| 第 8 週 | 常微分方程,神經模型的數值方法 Ordinary differential equations, numerical methods to neural models |
| 第 9 週 | 突觸傳遞、神經活動耦合、計算機模擬 Synaptic transmissions, coupling of neural activities, computer simulations |
| 第 10 週 | 神經建模的層次、計算機表示法、基礎資料結構 Levels of neural modeling, computer representations, basic data structure |
| 第 11 週 | 各種網絡模型,神經網絡的模擬 Assorted network models, simulations of neural networks |
| 第 12 週 | 突觸可塑性、可塑性規則與網絡穩定性 Synaptic plasticity, plasticity rules and network stability |
| 第 13 週 | 無監督學習、Hebbian學習、峰波時間相關的可塑性 Unsupervised learning, Hebbian learning, spike-timing dependent plasticity |
| 第 14 週 | 監督性學習,基礎人工神經網絡 Supervised learning, basic artificial neural networks |
| 第 15 週 | 行為調制、強化學習法、Q-學習法 Behavior conditioning, reinforcement learning, Q-learning |
| 第 16 週 | 分類問題、生成性模型、自組織性映射 Classification, generative models, self-organizing map |
| 第 17 週 | 神經功能、預測動力學、信息過濾、決策生成 Neural functions, predictive dynamics, information filtering, decision making |
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Dayan & Abbott (2001)
- 時間
- By appointment
- 聯絡方式
- cjj@uw.edu