Bonne nouvelle! Je suis désormais professeur adjoint au Département de mathématiques et informatique du Collège militaire royal du Canada à Kingston!
Au fil des ans, plusieurs étudiants intéressés aux neurosciences computationnelles m’ont contacté via mon blogue pour me poser des questions sur les différents parcours académiques disponibles à Montréal. Plusieurs chercheurs m’ont aussi contacté à propos […]
I have been invited to the reinforcement learning workshop whose theme was “Sculpting Representation for Reinforcement Learning”. The workshop was great and I thank the organizers (Rich Sutton, Doina Precup & Eliot Ludvig) for their invitation. You can find my slides on the workshop website.
Posted in MyResearch, Publications on December 7th, 2009 No Comments »
Rivest, Bengio & Kalaska
Learning timing in reinforcement learning model of dopamine responses in appetitive fixed-delay classical conditioning.
The temporal difference (TD) learning algorithm is now a common choice when modeling dopaminergic neuron activity in learning. When time is an important component of the experiment however, most models use timing elements, such as delay lines, that are […]
My most recent paper just appeared on-line:
Rivest, Kalaska, & Bengio (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience. doi: 10.1007/s10827-009-0191-1
Feel free to leave comments or questions here or to e-mail them to me.
Abstract: (c)Â Springer Science + Business Media, LLC 2009
Dopaminergic neuron activity has been modeled during learning and appetitive behavior, most commonly […]
I was invited for the third time (see last year post) as Guest Lecturer for the Cognitive Science course at McGill University this week. Here the material from the lecture, including PowerPointSlides, PDFSlides. The presentation was build around (Montague, Hyman, & Cohen, 2004). Thanks to professor Thomas Shultz and the great audience. Extra questions can […]
Posted in MyResearch on July 29th, 2009 2 Comments »
The world is dynamic, the brain is dynamic: There is no such thing called static processing. One can’t learn only from unordered pairs of inputs and outputs.
There is no unique St-Graal rule of learning: Learning is like linguistic, it has a number of sub-problems, for which there must be more specialised solutions, and these solutions […]
“Real-Time considerations are fundamental to natural intelligence.”
Klopf & Morgan (1990) The Role of Time in Natural Intelligence: Implications of classical and Instrumental Conditioning for Neuronal and Neural-Network Modeling. In Learning and Computational Neuroscience: Foundation of Adaptive Networks, Gabriel & Moore editors, p. 464.
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I was invited for the second time (see last year post) as Guest Lecturer for the Cognitive Science course at McGill University this week. As promised to the students in class, here’s all the material from the lecture, including Questions & References, PowePoint Slides, and PDF Slides (some slides contain extra references in notes). Sadly, […]
I was invited as Guest Lecturer for the Cognitive Science course at McGill University this week. As promised to the students in class yesterday, here’s all the material from the lecture, including MindManager Slides Map, Questions and useful references, PowerPoint Slides, and PDF Slides (some slides contain extra references in notes). Thanks to professor Thomas Shultz and the great […]
Je reviens tout juste du Computational Neuroscience Summer School et je voudrais en profiter pour remercier André Longtin ainsi que toute l’équipe du Centre for Neural Dynamics de l’Université d’Ottawa (et le commanditaire MITACS, organisme canadien d’excellence, pour le développement et le transfert des mathématiques) pour ces deux semaines de formation des plus agréables et […]