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
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