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X-WR-CALDESC:Events for ARNI
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DTSTART;TZID=UTC:20240307T133000
DTEND;TZID=UTC:20240307T144000
DTSTAMP:20260513T233955
CREATED:20240315T195437Z
LAST-MODIFIED:20240315T195437Z
UID:654-1709818200-1709822400@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/1906.01076\nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group-2/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240315T113000
DTEND;TZID=UTC:20240315T113000
DTSTAMP:20260513T233955
CREATED:20240314T195510Z
LAST-MODIFIED:20240314T195946Z
UID:627-1710502200-1710502200@arni-institute.org
SUMMARY:Rui Ponte Costa
DESCRIPTION:Title: Brain-wide credit assignment: cortical and subcortical perspectives \nAbstract: The brain assigns credit to trillions of synapses remarkably well. How the brain achieves this feat is one of the great mysteries in neuroscience. Recently\, we have introduced Bursting cortico-cortical networks\, a computational model of hierarchical credit assignment that captures a large number of biological features while approximating deep learning algorithms (Greedy et al. NeurIPS 2022). I will show that in contrast to previous work this model (i) does not require a multi-phase learning process\, (ii) is consistent with experimental observations across multiple levels and (iii) provides efficient credit assignment across the cortical hierarchy. \nHowever\, these models often assume that behavioural feedback is readily available. How the brain learns efficiently despite the sparse nature of feedback remains unclear. Recently we have proposed that a subcortical region\, the cerebellum\, predicts behavioural feedback\, thereby unlocking learning in cortical networks from future feedback. We have introduced two views by which the cerebellum may help the cortex: (i) by driving cortical plasticity (Boven et al. Nature Comms 2023) or (ii) by driving cortical dynamics (Pemberton et al. bioRxiv). Together these two views suggest that cortico-cerebellar loops are a critical part of task acquisition\, switching\, and consolidation in the brain.
URL:https://arni-institute.org/event/rui-ponte-costa/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240321T133000
DTEND;TZID=America/New_York:20240321T144000
DTSTAMP:20260513T233955
CREATED:20240314T201207Z
LAST-MODIFIED:20240322T003125Z
UID:637-1711027800-1711032000@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: \nPaper Topic: https://arxiv.org/abs/2102.01951 \nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240322T000000
DTEND;TZID=UTC:20240322T000000
DTSTAMP:20260513T233955
CREATED:20240315T190133Z
LAST-MODIFIED:20240319T225406Z
UID:643-1711065600-1711065600@arni-institute.org
SUMMARY:Jennifer Groh
DESCRIPTION:Title: Multiplexing multiple signals in neural codes: new statistical tools and evidence \nAbstract: How the brain represents multiple objects is mysterious. Sensory neurons are broadly tuned\, producing overlap in the populations of neurons potentially activated by each object in the scene. This overlap raises questions about how distinct information is retained about each item. I will present a novel theory of neural representation\, positing that neural signals may interleave representations of individual items across time. Evidence for this theory has come from new statistical tools that overcome the limitations inherent to standard time-and-trial-pooled assessments of activity. This theory has implications for diverse domains of neuroscience\, including attention\, figure-ground segregation\, and grounded cognition.
URL:https://arni-institute.org/event/jennifer-groh/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240322T150000
DTEND;TZID=UTC:20240322T170000
DTSTAMP:20260513T233955
CREATED:20240319T234436Z
LAST-MODIFIED:20240320T000555Z
UID:699-1711119600-1711126800@arni-institute.org
SUMMARY:Animal Behavior Video Analysis Working Group
DESCRIPTION:Title: Brain Decodes Deep Nets\nPresenter: Jianbo Shi\, PhD\nGRASP Laboratory\nComputer and Information Science\nUniversity of Pennsylvania\n \nAbstract: We developed a surprising usage of brain encoding: using a brain fMRI prediction model to draw a picture of how a deep net processes information onto a brain.  Our tool provides a detailed analysis of large pre-trained vision models by mapping them onto the brain\, thus exposing their hidden layers and channels.   Our results show how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior. It also provides insight into finetuning: how large pre-trained models change when adapting to new datasets. \n  \n\nJoin Zoom Meeting:\nhttps://columbiauniversity.zoom.us/j/93542681364?pwd=eFlZSkhGY0JHZGlHSk8zSVRYdHRSZz09 \nMeeting ID: 935 4268 1364\nPasscode: 645004
URL:https://arni-institute.org/event/animal-behavior-video-analysis-working-group-5/
LOCATION:CSB 453\, Mudd Building\, 500 W 120th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240328T133000
DTEND;TZID=UTC:20240328T144000
DTSTAMP:20260513T233955
CREATED:20240322T003508Z
LAST-MODIFIED:20240326T190536Z
UID:722-1711632600-1711636800@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: \nPaper Topic: https://arxiv.org/abs/2302.03241 \nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group-5/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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