NSCI 801: Quantitative Neuroscience

Syllabus (2019-2020)


Classroom:
Day, time:

Tutorial format, Matlab/Python based

The research process
Statistics and models in scientific discovery (Pearl)
Study design (power, sample size, effect size)
Interface
Basic syntax and commands
Data structures
Importing and manipulating data
Graphics
Vectors and Matrices
Functions
GUIs
Image processing
Data types
Sampling
DAQ
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
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 (fminsearch, BADS)
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)

Evaluation: pre-registration of research plan
(Pass / fail)



Further readings:

Please read this about academic integrity!