The primary objective of this course is to understand the principles of embodied cognition by taking a synthetic neurorobotics modeling approach. For this purpose, the course offers an introduction of related interdisciplinary knowledge in artificial intelligence and robotics, phenomenology, cognitive neuroscience, psychology, and deep and dynamic neural network models. Special focus is given to hands-on neurorobotics experiments and related term projects.
1. Introduction: cognitive neurorobotics study
2. Cognitism: compositionality and symbol grounding problem
3. Phenomenology: consciousness, free will and embodied minds
4. Cognitive neuroscience I: hierarchy in brains for perception and action
5. Cognitive neuroscience II: Integrating perception and action via top-down and bottom-up interaction
6. Affordance and developmental psychology
7. Nonlinear dynamical systems I: Discrete time system
8. Nonlinear dynamical systems II: Continuous time system
9. Neural network model I: 3-layered perceptron, recurrent neural network
10. Neural network model II: deep learning, variational Bayes
11. Neurorobotics I: affordance & motor schema
12. Neurorobotics II: higher-order cognition, meta-cognition, and consciousness
13. Neurorobotics III: hands-on experiments in lab
14. Paper reading for neurorobotics and embodied cognition I
15. Paper reading for neurorobotics and embodied cognition II
Basic mathematical knowledge for the calculus of vectors and matrices and the concept of differential equations are assumed. Programming experience in Python, C or C++ is also required.