神經科學資料分析
Data Analysis in Neuroscience
| 節 | 週三 |
|---|---|
3 10:10–11:00 | 神經科學資料分析 YL839（陽明） 2 節連堂 |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course is intended to provide an overview on the statistical, analytic, and computational tools that are commonly used in the research field of neuroscience. It will focus on characterizing the strength and applicability of various data analytical approaches in some more intuitive than formal ways. Through practical, simplified exercises, it aims to initiate students with tools that are likely to be useful for their future research in the field. The curriculum follows the core textbook and is structured around key data questions: * Statistical Inference: What is the data telling us? * Regression Analysis: Identifying trends and relationships. * Classification: Formulating questions and extracting answers. * Model Complexity & Selection: Avoiding over-interpretation of data. * Clustering & Density Estimation: Characterizing the "shape" of data. * Dimensionality Reduction: Filtering noise to find essential features. * Linear Time Series: Understanding cumulative changes over time. * Nonlinear Time Series: Exploring the "chemistry" between interacting variables. * Nonlinear Dynamical Systems: Analyzing how complex moving parts interact and evolve. Programming & Prerequisites All demonstrations and practical implementations will use Python. While prior experience with Python is beneficial, it is not a prerequisite; the course is designed to support students as they build these computational skills.
Required: effective English communication ability Helpful: basic calculus, linear algebra, and programming experience
無備註
Lectures follow the main textbook, with homework typically assigned upon completing each chapter. You will have one week to complete each assignment. Please submit your work as a single `.ipynb` file via the E3 digital learning platform, unless otherwise specified.
Homework (90%) Take-home final (10%)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course introduction Statistical models Model-based analysis Parameter estimation 2026-02-25(三) 時數：[2026-02-25]陳俊仲(2.00) |
| 第 2 週 | Optimizations: gradient descent, expectation maximization Hypothesis testing 2026-03-04(三) 時數：[2026-03-04]陳俊仲(2.00) |
| 第 3 週 | Multiple linear regression General linear model Multivariate regression Canonical correlation analysis 2026-03-11(三) 時數：[2026-03-11]陳俊仲(2.00) |
| 第 4 週 | Ridge & LASSO regression Local linear regression Basis expansions & splines k-nearest neighbor method Artificial neural networks and nonlinear regression 2026-03-18(三) 時數：[2026-03-18]陳俊仲(2.00) |
| 第 5 週 | Discriminant analysis Fisher’s discriminant criterion Logistic regression KNN for classification 2026-03-25(三) 時數：[2026-03-25]陳俊仲(2.00) |
| 第 6 週 | Maximum margin classifiers Kernel functions Support vector machines 2026-04-01(三) 時數：[2026-04-01]陳俊仲(2.00) |
| 第 7 週 | Model complexity and selection 2026-04-08(三) |
| 第 8 週 | Gaussian mixture models Density estimation Clustering: K-means and k-medoids 2026-04-15(三) 時數：[2026-04-15]陳俊仲(2.00) |
| 第 9 週 | Hierarchical cluster analysis Number of classes determination Mode hunting 2026-04-22(三) 時數：[2026-04-22]陳俊仲(2.00) |
| 第 10 週 | Principal component analysis Factor analysis Multidimensional scaling Locally linear embedding Independent component analysis 2026-04-29(三) 時數：[2026-04-29]陳俊仲(2.00) |
| 第 11 週 | Linear time series analysis: Autocorrelation, Power spectrum White noise, stationarity, and ergodicity Multivariate series Linear Models 2026-05-06(三) 時數：[2026-05-06]陳俊仲(2.00) |
| 第 12 週 | Multivariate AR model Statistical inference of model parameters Count and point process Implementation of multivariate AR model Granger causality AR and CCA calculations for Granger causality 2026-05-13(三) 時數：[2026-05-13]陳俊仲(2.00) |
| 第 13 週 | Linear series with latent variables: State-space models Gaussian-process factor analysis Count and point series Bootstrapping for time series 2026-05-20(三) 時數：[2026-05-20]陳俊仲(2.00) |
| 第 14 週 | Nonlinear concepts in time series analysis Detecting nonlinearity Nonparametric modeling Change point analysis Hidden Markov model 2026-05-27(三) 時數：[2026-05-27]陳俊仲(2.00) |
| 第 15 週 | Nonlinear dynamical systems Map dynamics Recurrent neural networks Differential equations Attractors & chaos 2026-06-03(三) 時數：[2026-06-03]陳俊仲(2.00) |
| 第 16 週 | Nonlinear oscillations Phase-locking Chaotic systems 2026-06-10(三) 時數：[2026-06-10]陳俊仲(2.00) |
Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models, Durstewitz, 2017. (Main, available online) Analysis of Neural Data, Kass, 2014. (Optional)
- 地點
- Online with with Google Meet.
- 時間
- Fridays 10am~11am by appointment.
- 聯絡方式
- Make an appointment by email by the Thursday evening.