======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: [[202009cns|2020]], [[202109cns|2021]], [[2022cns|2022]], [[2023cns|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