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
Statistical inference: What is the data telling us? Regression problems: Possible trends of the data. Classification problems: Asking questions and finding answers in the data. Model complexity and selection: Are we reading too much from the data? Clustering and density estimation: Characterize the "shapes" of the data. Dimensionality reduction: Cut out irrelevance, finding the most important. Linear time series analysis: Things that add up to the changing data. Nonlinear concepts in time series analysis: Chemistry between the causes of change. Time Series from a Nonlinear Dynamical Systems Perspective: The ways that moving things can mingle and what we will see as results.
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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.
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Course introduction; Statistical models; Model-based analysis; Parameter estimation 2021/02/24 |
| 第 2 週 | Optimizations: gradient descent, expectation maximization; Hypothesis testing 2021/03/03 |
| 第 3 週 | Regression: linear model, multivariate; Correlation analysis; Basis expansion; k-nearest neighbor method 2021/03/10 |
| 第 4 週 | Nonlinear regression and artificial neural networks; Logistic regression 2021/03/17 |
| 第 5 週 | Classification, discriminant analysis; Maximum margin classifier, kernel functions; Support vector machines 2021/03/24 |
| 第 6 週 | Model complexity; Cross-validation and Bootstrapping for estimating test error 2021/03/31 |
| 第 7 週 | Sampling in high dimensional space; Variable selection 2021/04/07 |
| 第 8 週 | Density estimation: Gaussian mixture model, Kernel density estimation 2021/04/14 |
| 第 9 週 | Clustering: K-mean and K-medoids; Hierarchical cluster analysis; Number of classes determination; Mode analysis 2021/04/21 |
| 第 10 週 | Principal component analysis; Factor analysis; Multidimensional scaling and locally linear embedding; Independent component analysis 2021/04/28 |
| 第 11 週 | Autocorrelation; power spectrum, white noise; Stationarity and edgodicity; Multivariate models 2021/05/05 |
| 第 12 週 | Autoregressive models for point processes; Granger Causality 2021/05/12 |
| 第 13 週 | Linear state space; Gaussian process factor; Latent variables for point processes 2021/05/19 |
| 第 14 週 | Computational and neurocognitive time series; Bootstrapping time series 2021/05/26 |
| 第 15 週 | Nonlinearity detection and nonparametric forecasting; Nonparametric time series modeling 2021/06/02 |
| 第 16 週 | Change point analysis; Hidden Markov models 2021/06/09 |
| 第 17 週 | Nonlinear dynamical systems; Univariate maps for discrete time systems; Multivariate maps and recurrent neural networks 2021/06/16 |
| 第 18 週 | Differential equations for dynamical systems; Nonlinear oscillation and phase locking 2021/06/23 |
"Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models" by Durstewitz (2017)
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