Computational Neuroscience

This course covers fundamental aspects of computational approaches to neuroscience problems, including: analytical modeling, numerical calculations, data processing, visualization, and functional applications.

Years taught: 2020, 2021, 2022, 2023

• Two lecture hours one tutorial hour each week
• Homework will be assigned about weekly
• Occasional quizzes will base on reading assignments
• Final examine (possibly take home) in last week

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Dayan & Abbott (2001)

• Basics on tools
  • Programming in python
  • Linear algebra
  • Differential equations
  • Information theory
• Neural representations
  • Spike trains and firing rates
  • Spike-trigger average and correlation
  • Tuning curve and receptive fields
  • Discrimination and inference
  • Mutual information and entropy
• Modeling neural circuits
  • Spiking neurons
  • Synaptic transmissions
  • Neuronal networks
  • Details and abstractions
• Functions
  • Adaptive dynamics and plasticity
  • Information filtering and prediction
  • Decision making
  • Learning in neural networks