This course will discuss how we can use computational techniques study the brain. Students will learn how to use concepts and algorithms from graph theory, data mining, and machine learning to study the structure and function of brain networks and gene and protein activities in healthy brains. The course will also cover how these ideas can be used to understand diseases of the brain, developmental disorders, and psychiatric illnesses and find treatments for them..

• History of neuroscience
• Rationale for networks
• Network measures
• Structure and dynamics of brain networks
• Mapping cells and circuits in the brain
• Small world networks
  Motifs, modules, and hubs
• Economy and efficiency
• Dynamic patternsd in neural activity
• Diseases in brain network
• Network growth and development
• Network stability and diversity
• Neural complexity

Prerequisite: junior or senior standing in computer science, neuroscience, 
or related fields. Familiarity and ease with at least one programming 
language will be very beneficial, as will be knowledge of basic data 
structures. Knowledge of graph algorithms is not a pre-requisite, but will 
be helpful. The course does not presume that students will have knowledge 
of neuroscience. Students are welcome to discuss their backgrounds with the instructor.