NSCI 801: Quantitative Neuroscience

Syllabus (2022-2023)

Classroom: hybrid - Sutherland 554 and Zoom (see OnQ for link)
Day, time: Winter term, Fridays, 1-4pm

Instructors: Gunnar Blohm, Joe Nashed

Tutorial format, Python based. We will use Google Colab. Recommended pre-requisite for this course is basic knowledge of Python - see this course.

All teaching materials are available on the Blohm lab Github page.

The research process
Statistics and models in scientific discovery (Pearl)
Study design (power, sample size, effect size)
Google Colab interface
Basic syntax and commands
Importing and manipulating data
Vectors and Matrices
Data types
Filtering (noise, differentiation, integration)
Time vs frequency analysis
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
ANOVA (between-subject, factorial, within-subject/repeated measures)
Measuring effect size
Multiple regression
Non-parametric tests
Image processing
Motivation and pitfalls of classic methods
Conditional probabilities and Bayes rule
Bayes Factor
Maximum A Posteriori (MAP) estimation
Bayesian ANOVA
Models in scientific discovery (Pearl)
Usefulness of models
Parameter search (Newton) and model fitting methods
Promises and limitations (Pearl)
Data organization (format, DB)
Blind data processing: machine learning techniques (classification, dimensionality reduction, decoding)
What’s causality?
How to achieve causality
Problem of unobserved variables in high-dimensional problems
Statistical considerations (multiple comparisons, exploratory analysis, hypothesis testing)
Open Science methods
Open science vs patents (required for drug discovery)

Course evaluation

(Virtual) pre-registration of research plan (i.e. stage 1 of registered report). Note, an actual pre-registration is not required (but encouraged); rather it is about generating the pre-registration materials, i.e. proposal summary, literature review, justified hypotheses, experimental approach and analysis plan (statistics).

Preliminary submission (for round of formative feedback, optional): April 7, 2023
Final due date: April 28, 2023

Follow Stage 1 instructions of registered reports!
See Center for Open Science for more info.

Additional guidelines:

Further readings:

Check out Statistics Learning Resources on the Blohm Lab WIKI.

Please read this about academic integrity!