BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARNI - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260605T160000
DTEND;TZID=America/New_York:20260605T170000
DTSTAMP:20260605T004136
CREATED:20260604T201257Z
LAST-MODIFIED:20260604T201341Z
UID:2549-1780675200-1780678800@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings \nZoom: request @ arni@columbia.edu
URL:https://arni-institute.org/event/select-arni-biological-learning-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260608T150000
DTEND;TZID=America/New_York:20260608T160000
DTSTAMP:20260605T004136
CREATED:20260601T193651Z
LAST-MODIFIED:20260601T193651Z
UID:2523-1780930800-1780934400@arni-institute.org
SUMMARY:Speaker: Mia Dai – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Speakers: Mia Dai\, PhD student in the Paninski Group at Columbia University \nTitle: Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding \nAbstract: A deeper understanding of brain function requires a precise\, structured characterization of behavior. Yet\, capturing behavior from video in a form suitable for scientific analysis remains a fundamental challenge. Many prior studies represent behavior via pose estimation or nonlinear video embeddings. However\, pose tracking discards rich information beyond predefined keypoints\, while nonlinear video embeddings lack interpretability. We address this limitation with SABLE (Sparse-view Animal Behavior Latent Embeddings)\, a self-supervised framework that leverages geometric inductive biases to learn behavior representations. By augmenting a multi-view transformer with priors from monocular depth and pose estimation\, SABLE reconstructs 3D animal behavior from extremely sparse views while learning explicit 3D latent structure. Without ground-truth 3D labels\, it reliably recovers 3D behavior from two-view videos\, whereas state-of-the-art methods fail or yield degenerate solutions. Through comprehensive evaluation\, we demonstrate that SABLE learns 3D representations that match or exceed prior SOTA performance in neural encoding and decoding. Our method establishes 3D-aware video embeddings that capture complex behavior\, opening new avenues for studying brain-behavior relationships. \nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-mia-dai-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260626T113000
DTEND;TZID=America/New_York:20260626T130000
DTSTAMP:20260605T004136
CREATED:20260501T143846Z
LAST-MODIFIED:20260501T143846Z
UID:2482-1782473400-1782478800@arni-institute.org
SUMMARY:CTN: Bence Olveczky (Harvard University) - June 26\, 2026
DESCRIPTION:Bence Olveczky \nLocation: ZI\nDate: June 26\, 2026\nTime: 11:30am\nZoom: Upon Request @ arni@Columbia.edu \nTitle and Abstract: TBD
URL:https://arni-institute.org/event/ctn-bence-olveczky-harvard-university-june-26-2026/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260909T150000
DTEND;TZID=America/New_York:20260909T160000
DTSTAMP:20260605T004136
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
END:VEVENT
END:VCALENDAR