Skip to content
NSCI 801 (Queen's U) Quantitative Neuroscience course materials
Jupyter Notebook
Branch: master
Clone or download
Cannot retrieve the latest commit at this time.
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Figures
Practice Add files via upload Jan 22, 2020
stuff new lecture 1 Jan 8, 2020
.gitignore
LICENSE
NSCI801_Intro.ipynb
NSCI801_acquisition_filters.ipynb
NSCI801_advanced_python.ipynb
NSCI801_intro_python.ipynb
README.md

README.md

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)

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)
You can’t perform that action at this time.