This course discusses cognitive information processing mechanism in our brain and computational models for human-like cognitive systems. We will first discuss neural data representation and move to the models of perception, attention, socialization, memory, learning, reasoning, and problem-solving.
This course handles underlying background theories for pattern recognition (PR) which is the start point for AI. It covers PR systems, Bayesian Classifier, likelihood-based PR, Discriminant Function-based PR, Support Vector Machine, NN-based PR, and other PR theories such as fuzzy theory, and so on.
The primary objective of this course is to discuss what NeuroImaging methods are available to study the brain. The focus of the course will be on modern tools capable of whole-brain imaging (mostly MRI), but we will also discuss non-MRI techniques as well. As part of the term project, students will be asked to propose novel acquisition and/or analysis method that is likely to facilitate our ability to understand the brain.
The primary objective of this course is to explore the synthetic modeling approach to understand brain-based mechanisms for learning and generating sensory-motor behaviors. For this purpose, the course will offer introduction of neuro-robotics studies as well as neuroscience literature related to brain mechanisms for sensory perception and behavior generation. In addition, the course will offer hands-on experiences on experimenting neuro-driven learnable robots in the instructor’s lab. The course will gain a good understanding of mechanisms for learning and generating cognitive behaviors both in biological brains and artifacts. Evaluation is based on quizzes during class, term project, and active class participation.