## NSCI 801: Quantitative Neuroscience

### Syllabus (2023-2024)

Classroom: see Solus or OnQ
Day, time: see Solus or OnQ

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.

• Week 1: Introduction (Gunnar)
The research process
Statistics and models in scientific discovery (Pearl)
Study design (power, sample size, effect size)
• Week 2: Intro Python (Joe)
Basic syntax and commands
Importing and manipulating data
Graphics
• Week 3: Advanced Python (Joe)
Vectors and Matrices
Functions
• Week 4: Data collection / signal processing (Joe)
Data types
Sampling
DAQ
Filtering (noise, differentiation, integration)
Time vs frequency analysis
• Week 5: 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
• Week 6: Statistics and Hypothesis testing - advanced (Joe)
ANOVA (between-subject, factorial, within-subject/repeated measures)
Measuring effect size
Multiple regression
Non-parametric tests
• Week 7: Quantitative wet lab / bench methods (Joe)
Image processing
• Week 8: 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
• Week 9: Models in Neuroscience (Gunnar)
Models in scientific discovery (Pearl)
Usefulness of models
Parameter search (Newton) and model fitting methods
• Week 10: Data Neuroscience overview (Gunnar)
Promises and limitations (Pearl)
Data organization (format, DB)
Blind data processing: machine learning techniques (classification, dimensionality reduction, decoding)
• Week 11: Correlation vs causality (Gunnar)
What’s causality?
How to achieve causality
Problem of unobserved variables in high-dimensional problems
• Week 12: Reproducibility, reliability, validity (Gunnar)
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 5, 2024
Final due date: April 26, 2024

Follow Stage 1 instructions of registered reports!