Plenary lecture of ICONIP 2017
What can we further learn from the brain?
Kenji Doya (Neural Computation Unit,
Okinawa Institute of Science and Technology Graduate University)
Okinawa Institute of Science and Technology Graduate University)
Deep learning is a prime example of how brain-inspired computing architecture can benefit artificial intelligence. But what else can we learn from the brain for bringing artificial intelligence to the next level? The brain can be seen as a multi-agent system composed of heterogeneous learners using different representations and algorithms. In navigation and control, the use of allocentric, egocentric, and intrinsic state representations offer different advantages. In reinforcement learning, the choice or mixture of model-free and model-based algorithms critically affects data efficiency and computational costs. Animals and humans appear to be able to utilize multiple representations and algorithms in highly flexible ways. How the brain realizes flexible selection and combination of relevant modules for a given situation is a major open problem in neuroscience and its solution should help developments of more flexible, general artificial intelligence.
- Paper Submission Deadline
June 15, 2017
June 30, 2017 - Notification of Acceptance
July 31, 2017 - Final Paper Submission
August 20, 2017 - Congress Date
November 14–18, 2017
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