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X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250304T090000
DTEND;TZID=America/New_York:20250304T170000
DTSTAMP:20260424T105957
CREATED:20250303T214649Z
LAST-MODIFIED:20250303T214859Z
UID:1547-1741078800-1741107600@arni-institute.org
SUMMARY:Columbia AI Summit
DESCRIPTION:Columbia University is bringing its community together for an exhilarating\, day-long exploration of artificial intelligence and its transformative impact across disciplines. Across the Morningside\, Manhattanville\, and Medical Center campuses\, specialized workshops will dive deep into AI’s role in fields ranging from healthcare to the humanities. The event will feature a must-see keynote by Sami Haddadin\, Director of the Munich Institute of Robotics and Machine Intelligence and Vice President for Research at MBZUAI. \nLink: https://ai.columbia.edu/ai-summit#!#text-1655
URL:https://arni-institute.org/event/columbia-ai-summit/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250307T113000
DTEND;TZID=America/New_York:20250307T130000
DTSTAMP:20260424T105957
CREATED:20250303T213800Z
LAST-MODIFIED:20250305T185758Z
UID:1536-1741347000-1741352400@arni-institute.org
SUMMARY:CTN: Tim Buschman
DESCRIPTION:Title: The geometry of cognitive flexibility \n\nAbstract: Humans and animals are remarkably good at multi-tasking: we quickly learn many different tasks and flexibly switch between them. Theoretical work suggests such cognitive flexibility requires representing the current task and then using this task representation to selectively engage in task-relevant computations. In this talk\, I will discuss recent research from my lab aimed at understanding the neural mechanisms underlying cognitive flexibility. I will discuss how tasks are represented in the brain and how new task representations can be learned. I will also discuss how the brain flexibly re-uses neural representations of sensory inputs and motor actions across different tasks. This allows the brain to compositionally construct complex tasks from simpler sub-tasks by routing task-relevant information between subspaces of neural activity.
URL:https://arni-institute.org/event/ctn-tim-buschman/
LOCATION:Zuckerman Institute – L7-119\, 3227 Broadway\, New York\, NY\, 10027\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250311T160000
DTEND;TZID=America/New_York:20250311T170000
DTSTAMP:20260424T105957
CREATED:20250307T145746Z
LAST-MODIFIED:20250307T145746Z
UID:1560-1741708800-1741712400@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Continuation of Year 3 proposal meeting! \nZoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250314T113000
DTEND;TZID=America/New_York:20250314T130000
DTSTAMP:20260424T105957
CREATED:20250303T214002Z
LAST-MODIFIED:20250312T142728Z
UID:1539-1741951800-1741957200@arni-institute.org
SUMMARY:CTN: Christian Machens
DESCRIPTION:Title: Computing with spikes: A geometric approach\n\nAbstract: How can recurrent spiking networks perform computations in a biologically realistic regime? I will outline the progress we have made in answering this question. Our approach follows two principles. First\, we don’t average over spikes\, but focus on the contribution of each individual spike. Second\, we study the decision to spike in a low-dimensional space of latent population modes (or readouts\, components\, factors\, you name it) rather than in the original neural space. Neural thresholds then become convex boundaries in latent space\, and the latent dynamics is either attracted (I population) or repelled (E population) by these boundaries. The combination of E and I populations results in balanced\, inhibition-stabilized networks which are capable of producing (arbitrary) dynamical systems or input-output mappings. Moreover\, there are profound differences between computation in these spiking networks compared to classical rate networks. I will illustrate all of these insights through geometrical pictures and movies and thereby demonstrate that we are far from having exhausted analytical and geometric methods in understanding recurrent spiking neural networks [joint work with William Podlaski].
URL:https://arni-institute.org/event/ctn-christian-machens/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250319T113000
DTEND;TZID=America/New_York:20250319T130000
DTSTAMP:20260424T105957
CREATED:20250319T141153Z
LAST-MODIFIED:20250319T141153Z
UID:1578-1742383800-1742389200@arni-institute.org
SUMMARY:CTN: Soledad Gonzalo Cogno
DESCRIPTION:Soledad Gonzalo Cogno \nSeminar Time: 11:30am \nDate: Wed 3/19/25 \nLocation: JLG\, L5-084 \nTitle: Ultraslow patterns of neural population activity in the entorhinal-hippocampal circuit \nNote: Everything I will present in this talk is preliminary – Feedback and ideas will be very much appreciated! \nAbstract: The medial entorhinal cortex hosts many of the brain’s circuit elements for spatial navigation and episodic memory\, operations that require neural activity to be organized across long durations of experience. We have previously found that entorhinal cells can organize their activity into ultraslow oscillations (frequency < 0.1 Hz) that manifest as periodic sequences of activity in the neural population (Gonzalo Cogno et al.\, 2024). These ultraslow periodic sequences were recorded while mice ran at free pace on a rotating wheel in darkness\, with no change in running direction and no scheduled rewards. It remains unknown\, however\, whether the sequences also occur during more naturalistic behaviours\, for example while mice run in an open field arena\, or during sleep. In this presentation I will show that in free foraging conditions\, MEC neuronal activity can organize into sequences. However\, the sequential activity is now characterized by resettings and interruptions. By developing a computational model\, we investigate the conditions under which the sequences reset. In addition\, we found that during slow-wave-sleep neural activity is also organized into ultraslow oscillations\, but not into sequences. The oscillations also manifest in the hippocampus\, and are highly synchronized with those in the MEC. These results suggest the presence of internal dynamics that unfold at ultraslow time scales\, and that are modulated by sensory information and cognitive demands. \nBecause oscillations and sequences are not the only way into which neural activity can organize at ultraslow time scales\, we next sought to determine whether other slowly changing patterns of activity are present in the MEC. If those exists\, it is yet an open question whether\, and how\, those are transformed in the hippocampal-entorhinal circuit. We found that when animals ran at free pace on a rotating wheel in darkness\, the activity in the MEC\, lateral entorhinal cortex (LEC) and hippocampus slowly drifted over session time\, enabling a readout of episodic time. However\, the drift in the MEC and the hippocampus\, but not in the LEC\, significantly decreased when animals ran in an open field arena. These results suggest that the slow drift of hippocampal and MEC activity is attenuated by spatial landmarks when these are present. \nAll in all\, our results point to the existent of ultraslow dynamics in the entorhinal-hippocampal circuit that may facilitate the encoding of experience at behavioral time scales.
URL:https://arni-institute.org/event/ctn-soledad-gonzalo-cogno/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250321T110000
DTEND;TZID=America/New_York:20250321T130000
DTSTAMP:20260424T105957
CREATED:20250303T214301Z
LAST-MODIFIED:20250303T214301Z
UID:1542-1742554800-1742562000@arni-institute.org
SUMMARY:CTN: Anna Levina and
DESCRIPTION:Title: TBD \nAbstract: TBD
URL:https://arni-institute.org/event/ctn-anna-levina-and/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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