

Course ID:  CSCI 4860/6860. 4 hours. 
Course Title:  Computational Neuroscience 
Course Description:  Introduction to computational neuroscience. Students will learn basic concepts, algorithms, and software tools for computational neuroscience models. Neural signal processing and neural network models will be discussed. 
Oasis Title:  Computational Neuroscience 
Prerequisite:  CSCI 2720 or CSCI 2725 or permission of department 
Semester Course Offered:  Not offered on a regular basis. 
Grading System:  AF (Traditional) 

Course Objectives:  This course aims to teach basic concepts and algorithms for computational neuroscience models. The course will cover neural signal processing and neural network modeling. Multidimensional neural signal processing and neural network modeling methods will be discussed in the contexts of neurobiology, brain imaging, cognitive neuroscience, and clinical neuroscience. 
Topical Outline:  1. Introduction to brain science
1.1 Introduction to neurons
1.2 Introduction to axons and their wiring
1.3 Introduction to communication mechanisms among neurons and their networks
1.4 Introduction to basic brain science principles
2. Neural signal processing
2.1 Basic concepts in neural signal formation
2.2 Multidimensional neural signal formation and reconstruction
3. Neural signal representation
3.1 1D signal representation such as EEG and MEG
3.2 2D signal representation such as neurobiological images
3.3 3D signal representation such as neuroimaging data
3.4 4D signal representation such as fMRI data
4. Neural signal transform, modeling, and analysis
4.1 Neural signal transform and filtering
4.2 Modeldriven neural signal modeling and analysis
4.3 Datadriven neural signal modeling and analysis
4.4 Hybrid neural signal modeling and analysis
5. Neural network models
5.1 Classical neural network models in neurophysiology
5.2 Structural neural network models
5.3 Functional neural network models
5.4 Multidimensional and multimodal neural network models
5.5 Interface between artificial neural networks and biological neural networks
5.6 Interface between deep learning and neural network models
5.7 Abstraction of common graph models in both artificial and biological neural networks 