NSCI 801 - Quantitative Neuroscience
NSCI 801 (Queen's U) Quantitative Neuroscience course materials
This course is in tutorial format using Python and Google Colab.
Syllabus
Introduction (Gunnar)
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The research process
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Statistics and models in scientific discovery (Pearl)
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Study design (power, sample size, effect size)
Intro Python (Joe)
- Google Colab interface
- Basic syntax and commands
- Importing and manipulating data
- Graphics
Advanced Python (Joe)
- Vectors and Matrices
- Functions
Data collection / signal processing (Joe)
- Data types
- Sampling
- DAQ
- Filtering (noise, differentiation, integration)
- Time vs frequency analysis
Statistics and Hypothesis testing - basics (Joe)
- Descriptors: central tendencies (mean, median, mode), Spread (Range, variance, percentiles), Shape (skew, kurtosis)
- Correlation / regression
- The logic of hypothesis testing
- Statistical significance
- Multiple comparisons
- Different test statistics
- Confidence intervals and bootstrap
Statistics and Hypothesis testing - advanced (Joe)
- ANOVA (between-subject, factorial, within-subject/repeated measures)
- Measuring effect size
- Multiple regression
- Non-parametric tests
Quantitative wet lab / bench methods (Joe)
- Image processing
Statistics and Hypothesis testing - Bayesian (Gunnar)
- Motivation and pitfalls of classic methods
- Conditional probabilities and Bayes rule
- Bayes Factor
- Maximum A Posteriori (MAP) estimation
- Bayesian ANOVA
Models in Neuroscience (Gunnar)
- Models in scientific discovery (Pearl)
- Usefulness of models
- Parameter search (Newton) and model fitting methods
Data Neuroscience overview (Gunnar)
- Promises and limitations (Pearl)
- Data organization (format, DB)
- Blind data processing: machine learning techniques (classification, dimensionality reduction, decoding)
Correlation vs causality (Gunnar)
- What’s causality?
- How to achieve causality
- Problem of unobserved variables in high-dimensional problems
Reproducibility, reliability, validity (Gunnar)
- Statistical considerations (multiple comparisons, exploratory analysis, hypothesis testing)
- Open Science methods
- Open science vs patents (required for drug discovery)