After successful completion of the course, students:
- Know about current developments and debates regarding the modeling and understanding of natural intelligence
- Are able to implement computational models of cognitive processs in the Python programming language
- Can conduct independent research in computational cognitive neuroscience
|
|
A main objective of artificial intelligence is to build machines whose cognitive abilities match (or surpass) those of humans. This is also referred to as strong AI. One way to achieve this goal is by developing cognitive architectures that implement the algorithms used by our own brains. This success of such an approach relies on a continuous interplay between AI and neuroscience.
In this course, we will explore how computational models, particularly neural networks, can yield new insights about the mechanisms that give rise to natural intelligence and provide us with the tools to model cognitive processes in artificial systems.
The course consists of different components:
- During the lectures, students will get acquainted with the formal aspects and practical development of computational models of cognitive processes. They will learn about the current state of research concerning the modeling and understanding of natural intelligence.
- Students will present key papers on the state of the art in class themselves.
- During the practical sessions, students will learn to write computer programs related to specific topics discussed in class. To this end, the Python programming language will be used.
- In the final part of the course, students will formulate their own research project. The outcome of the research project should be a working computational model accompanied by a NIPS style conference paper that provides original insights about cognitive processing in artificial and/or biological agents.
|
|
|