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X-WR-CALDESC:Events for ARNI
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DTSTART;TZID=America/New_York:20260630T150000
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DTSTAMP:20260617T153149
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UID:2566-1782831600-1782835200@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Continuation from prior meetings. \nZoom: Upon request @ arni@columbia.edu \n 
URL:https://arni-institute.org/event/arni-continual-learning-working-group-6/
LOCATION:Virtual
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260802T100000
DTEND;TZID=America/New_York:20260802T180000
DTSTAMP:20260617T153149
CREATED:20260610T192956Z
LAST-MODIFIED:20260610T192956Z
UID:2563-1785664800-1785693600@arni-institute.org
SUMMARY:CCN 2026 Satelite Event: Modeling and Understanding Human Brain Computation at Scale
DESCRIPTION:Location: Zuckerman Institute (room TBD)\nDate and Time: August 2nd\, 10am to 6pm \nAbout:  \nRecent advances in deep learning and improvements in the quantity and quality of available human brain-activity data (including functional MRI\, EEG\, MEG\, and intracranial recordings) have made it possible to build accurate encoding models of the human brain that can predict neural activity for new visual and auditory stimuli in individual people\, even with generalization to new individuals. In parallel\, recent decoding models leverage prior information from generative multimodal models to extract rich perceptual and semantic content from brain activity with increasing fidelity. It remains unclear\, however\, how these technical advances can best be translated into theoretical advances (a better scientific understanding of human brain computation) and impactful applications for the benefit of humanity. \nOne important goal is to build human brain foundation models that are constrained simultaneously by rich stimulus data\, large-scale diverse brain-activity data\, and task performance requirements\, so as to capture the computations performed by the human brain. This satellite event “Modeling and Understanding Human Brain Computation at Scale” brings together researchers who build neural network models that capture shared structure in neural responses across the human population at scale and use the models to drive theoretical progress on the computations underlying human cognition and perception. A central theme is methodology: What mapping functions\, architectures\, and training regimes achieve strong generalization and enable interpretation? The event aims to foster dialogue between those collecting and modeling large-scale human brain data and those asking what such models can tell us about how the brain works and how human brain foundation models might be applied for human benefit.
URL:https://arni-institute.org/event/ccn-2026-satelite-event-modeling-and-understanding-human-brain-computation-at-scale/
LOCATION:To Be Determined
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DTSTART;TZID=America/New_York:20260909T150000
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DTSTAMP:20260617T153149
CREATED:20260501T134305Z
LAST-MODIFIED:20260527T143526Z
UID:2476-1788966000-1788969600@arni-institute.org
SUMMARY:Speaker: Alexandre Pouget - ARNI Distinguish Seminar Series
DESCRIPTION:Alexandre Pouget \nDate and Time: May 21st at 3pm \nLocation: Zuckerman Institute Kavli Auditorium 9th Floor \nTitle: Neural Models of Compositionality \nAbstract: Compositionality is widely regarded as one of the cornerstones of general intelligence. It refers to the ability to rapidly generate or learn new concepts by combining simpler ones according to an underlying syntax\, as exemplified in natural language. Compositionality was long thought to be primarily a human capacity and widely considered incompatible with artificial neural networks. Recent neural models\, however\, have begun to challenge this view. I will present two such models: one focused on simple cognitive tasks\, the other on the control of complex motor trajectories. In both cases\, few-shot learning emerges through the discovery of compositional solutions. Remarkably\, the latter approach captures key\, and often counterintuitive\, aspects of rodent behavior in escape tasks\, precisely the kind of setting in which animals exhibit near zero-shot learning. I will also discuss how these findings connect naturally to more sophisticated forms of compositionality in humans\, particularly the use of language to support zero-shot learning and inference. \nZoom link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-alexandre-pouget-arni-distinguish-seminar-series/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
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