NSCI 801 - Quantitative Neuroscience Syllabus

Contents

NSCI 801 - Quantitative Neuroscience Syllabus¶

NSCI 801 (Queen’s U) Quantitative Neuroscience course materials

This course is in tutorial format using Python and Google Colab.

Syllabus¶

Introduction (Gunnar)¶

The research process
Statistics and models in scientific discovery (Pearl)
Study design (power, sample size, effect size)

Intro slides (NSCI801_Intro.ipynb)

Intro Python (Joe)¶

Google Colab interface
Basic syntax and commands
Importing and manipulating data
Graphics

Intro Python (NSCI801_Intro_python.ipynb)

Advanced Python (Joe)¶

Vectors and Matrices
Functions

Advanced Python (NSCI801_Advanced_python.ipynb)

Data collection / signal processing (Joe)¶

Data types
Sampling
DAQ
Filtering (noise, differentiation, integration)
Time vs frequency analysis

Data Collection/Signal Processing (NSCI801_acquisition_filters.ipynb)

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

Descriptive Statistic (NSCI801_Descriptive_stats.ipynb)

Statistics and Hypothesis testing - advanced (Joe)¶

ANOVA (between-subject, factorial, within-subject/repeated measures)
Measuring effect size
Multiple regression
Non-parametric tests

Statistics and hypothesis testing (NSCI801_Advanced_stats.ipynb)

Quantitative wet lab / bench methods (Joe)¶

Image processing

Image Processing (NSCI801_Image_Processing_Proper_v2.ipynb)

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

Bayesian statistics (NSCI801_Bayesian-stats.ipynb)

Models in Neuroscience (Gunnar)¶

Models in scientific discovery (Pearl)
Usefulness of models
Model fitting
bootstrap

Model fitting (NSCI801_ModelFitting.ipynb)

Data Neuroscience overview (Gunnar)¶

Promises and limitations (Pearl)
Data organization (format, DB)
Blind data processing: machine learning techniques (classification, dimensionality reduction, decoding)

Data Neuroscience (NSCI801_DataNeuroscience.ipynb)

Correlation vs causality (Gunnar)¶

What’s causality?
How to achieve causality
Problem of unobserved variables in high-dimensional problems

Correlation vs causality (NSCI801_CorrelationVsCausality.ipynb)

Reproducibility, reliability, validity (Gunnar)¶

Statistical considerations (multiple comparisons, exploratory analysis, hypothesis testing)
Open Science methods
Open science vs patents (required for drug discovery)

Reproducibility, reliability, validity (NSCI801_Reproducibility.ipynb)

Further readings¶

10 common stats mistakes paper
Statistical Thinking for the 21st Century free online book by Russell A. Poldrack
see more here

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1. Introduction