Signal detection and Information Analyses in Neuroscience

Instructors: 

Mehdi Adibi-Sede

This course provides an introduction to fundamental analyses and methods for understanding computational principles governing various aspects of information processing in the brain, including examples from single sensory neurons to ensemble of neurons, and behavioural and psychological measures.

 

Syllabus

Introduction to Statistical Models

  • Probability and variation, distribution and moments
  • Random variables and stochastic processes (neuronal and behavioural examples)
  • Likelihood and Maximum Likelihood Estimators

Introduction to Signal Detection Theory

  • Noise and Signal
  • Discriminability index
  • ROC
  • Selectivity and sensitivity (neuronal and behavioural examples)
  • Covariation and Discriminant analyses (implications in decoding, neuronal examples)

Introduction to Information Theory

  • KL divergence (behavioural example)
  • Entropy
  • Mutual information (neuronal and behavioural examples)