The reinforcement learning algorithms, developed in the 80's to endow artificial agents with stronger autonomy, have had a deep influence on the study of decision-making in neuroscience after the discovery, in the 90's, of the striking similarity between these algorithms and the interactions of the cortex and basal ganglia loops with the dopaminergic system. A new exciting current in Artificial Intelligence (AI) and robotics is the development of deep learning methods. They have been steadily gaining momentum, and these have also been taught widely in the popular press as a watershed for modelling intelligence and the brain.
This workshop working group will put together views from the AI and robotics community and from neuroscience (computational and experimental) to address the following questions:
- Is state-of-the-art AI still in position of fruitfully inseminating neuroscience? Have deep learning methods the potential to lead to new success stories? How could these and other current methods be used to analyse data, and to formalise information processing principles in neural circuits?
- Conversely, are discoveries in computational neuroscience in position of feeding AI and robotics with new ideas? In these domains, is neuro-inspiration complementary to the traditional engineering approach? What might neuroscience bring to the deep learning community?
| Time |
October 26
F. Jacob Auditorium
|
| 10:00 |
Introduction and goals - Mehdi Khamassi, Boris Gutkin
Questions & discussion
|
| 10:10 |
Position statement by Samuel Gershman
Questions & discussion
|
| 10:35 |
Position statement by Haim Sompolinsky
Questions & discussion
|
| 11:00 | Coffee break |
| 11:30 | Position statement by Sophie Deneve |
| 11:55 | Position statement by Ron Meir |
| 12:20 | General discussion and conclusions |
| 13:00 | End of working group session |
