BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Memming Park (Champalimaud Foundation) DTSTART:20230323T170000Z DTEND:20230323T180000Z DTSTAMP:20260423T003250Z UID:MPML/100 DESCRIPTION:Title: O n learning signals in recurrent networks\nby Memming Park (Champalimau d Foundation) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nNeural dynamical systems with stable attractor str uctures such as point attractors and continuous attractors are widely hypo thesized to underlie meaningful temporal behavior that requires working me mory. However\, perhaps counterintuitively\, having good working memory is not sufficient for supporting useful learning signals that are necessary to adapt to changes in the temporal structure of the environment. We show that in addition to the well-known continuous attractors\, the periodic an d quasi-periodic attractors are also fundamentally capable of supporting l earning arbitrarily long temporal relationships. Due to the fine tuning pr oblem of the continuous attractors and the lack of\ntemporal fluctuations\ , we believe the less explored quasi-periodic attractors are uniquely qual ified for learning to produce temporally structured behavior. Our theory h as wide implications for the design of artificial learning systems\, and m akes predictions on the observable signatures of biological neural dynamic s that can support temporal dependence learning. Based on our theory\, we developed a new initialization scheme for artificial recurrent neural netw orks which outperforms standard methods for tasks that require learning te mporal dynamics. Finally\, we speculate on their biological implementation s and make predictions on neuronal dynamics.\n LOCATION:https://researchseminars.org/talk/MPML/100/ END:VEVENT END:VCALENDAR