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computational-neuroscience

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

Format

  • 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

Textbook

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

Syllabus

  • 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
computational-neuroscience.txt · Last modified: 2023/06/14 07:19 by cjj