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X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241003T130000
DTEND;TZID=America/New_York:20241003T150000
DTSTAMP:20260502T055944
CREATED:20240912T211938Z
LAST-MODIFIED:20241003T221343Z
UID:1039-1727960400-1727967600@arni-institute.org
SUMMARY:Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS): Simon Laughlin
DESCRIPTION:Title: Neuronal energy consumption: basic measures and trade-offs\, and their effects on efficiency \nZoom: https://columbiauniversity.zoom.us/j/98299154214?pwd=1J3J0lEpF6XdqHkHy02c7LuD6xUWx2.1
URL:https://arni-institute.org/event/multi-resource-cost-optimization-for-neural-networks-models-working-group-nnms-simon-laughlin/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241004T140000
DTEND;TZID=UTC:20241004T160000
DTSTAMP:20260502T055944
CREATED:20240924T223032Z
LAST-MODIFIED:20240924T223032Z
UID:1065-1728050400-1728057600@arni-institute.org
SUMMARY:Continual Learning Working Group: Amogh Inamdar
DESCRIPTION:Title: Taskonomy: Disentangling Task Transfer Learning \nAbstract: TBD  \nLink: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf
URL:https://arni-institute.org/event/continual-learning-working-group-amogh-inamdar/
LOCATION:CSB 488
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241011T113000
DTEND;TZID=America/New_York:20241011T130000
DTSTAMP:20260502T055944
CREATED:20240913T200808Z
LAST-MODIFIED:20241008T172933Z
UID:1049-1728646200-1728651600@arni-institute.org
SUMMARY:CTN: Brenden Lake
DESCRIPTION:Title: Meta-learning for more powerful behavioral modeling \nAbstract: Two modeling paradigms have historically been in tension: Bayesian models provide an elegant way to incorporate prior knowledge\, but they make simplifying and constraining assumptions; on the other hand\, neural networks provide great modeling flexibility\, but they make it difficult to incorporate prior knowledge. Here I describe how to get the best of both approaches through Behaviorally-Informed Meta-Learning (BIML). BIML allows for modeling behavior with flexible Transformers\, even with only minimal data\, by distilling Bayesian priors into neural networks and then further fine-tuning the networks on behavioral data. I’ll show some initial successes using BIML to model human concept learning\, resulting in superior fits by capturing behavioral heuristics and biases that violate simple Bayesian assumptions. At the end\, I would love to discuss how to overcome the challenges of interpreting this new class of models. \nZoom: https://columbiauniversity.zoom.us/j/93740145362?pwd=GgoanUbc3Kc4rWdux2doLOiciiAaO2.1\nmeeting ID: 937 4014 5362\npasscode: ctn
URL:https://arni-institute.org/event/ctn-brenden-lake/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241011T133000
DTEND;TZID=UTC:20241011T150000
DTSTAMP:20260502T055944
CREATED:20241004T201714Z
LAST-MODIFIED:20241004T201723Z
UID:1089-1728653400-1728658800@arni-institute.org
SUMMARY:Continual Learning Working Group: Lindsay Smith
DESCRIPTION:Title: A Practitioner’s Guide to Continual Multimodal Pretraining \nReading: https://arxiv.org/pdf/2408.1447 \nZoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/continual-learning-working-group-10/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241016T103000
DTEND;TZID=America/New_York:20241016T120000
DTSTAMP:20260502T055944
CREATED:20241011T201622Z
LAST-MODIFIED:20241011T201715Z
UID:1100-1729074600-1729080000@arni-institute.org
SUMMARY:CTN: Benjamin Grewe
DESCRIPTION:Title: Target Learning rather than Backpropagation Explains Learning in the Mammalian Neocortex \nAbstract: Modern computational neuroscience presents two competing hypotheses for hierarchical learning in the neocortex: (1) deep learning-inspired approximations of the backpropagation algorithm\, where neurons adjust synapses to minimize error\, and (2) target learning algorithms\, where neurons reduce the feedback required to achieve a desired activity. In this talk\, I will explore this fundamental question by examining the relationship between synaptic plasticity and the somatic activity of pyramidal neurons. Using a combination of single-neuron modeling\, in vitro experiments\, and deep learning theory\, we predict distinct neuronal dynamics for each hypothesis. We then test these predictions using in vivo data from the mouse visual cortex. Our results reveal that cortical learning aligns more closely with target learning\, underscoring a significant discrepancy between conventional deep learning approaches and the mechanisms underlying cortical hierarchical learning. This work provides new insights into the neural processes that drive learning in the brain and challenges current models inspired by deep learning.
URL:https://arni-institute.org/event/ctn-benjamin-grewe/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241018T133000
DTEND;TZID=UTC:20241018T150000
DTSTAMP:20260502T055944
CREATED:20241004T202033Z
LAST-MODIFIED:20251016T132455Z
UID:1094-1729258200-1729263600@arni-institute.org
SUMMARY:Continual Learning Working Group: Yasaman Mahdaviyeh
DESCRIPTION:  \nTitle: Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction \nReading: https://openreview.net/pdf?id=TpD2aG1h0D \nZoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/continual-learning-working-group-yasaman-mahdaviyeh/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241021T090000
DTEND;TZID=America/New_York:20241022T130000
DTSTAMP:20260502T055944
CREATED:20240913T201213Z
LAST-MODIFIED:20241011T202544Z
UID:1052-1729501200-1729602000@arni-institute.org
SUMMARY:ARNI Annual Retreat
DESCRIPTION:General Agenda \nOctober 21st\, Day 1 from 8:45am to 5pm \n\nBreakfast and Lunch Provided\nOpening\n3 Keynote Speakers from ARNI Faculty\nResearch Brainstorming and Discussions\nProject/Student Poster Session\nEducation and Broader Impact Discussions\n\nOctober 22nd\, Day 2 from 9am to 1pm \n\nBreakfast and Lunch Provided\n1 Keynote Speaker\nBrainstorming and Discussion on Collaborations & Knowledge Transfer\nAI Industry Panel (with industry partners)\nClosing
URL:https://arni-institute.org/event/arni-annual-retreat/
LOCATION:Faculty House\, 64 Morningside Dr
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241025T113000
DTEND;TZID=UTC:20241025T130000
DTSTAMP:20260502T055944
CREATED:20241022T222511Z
LAST-MODIFIED:20241022T224826Z
UID:1109-1729855800-1729861200@arni-institute.org
SUMMARY:CTN: Tatiana Engel
DESCRIPTION:Title: Unifying neural population dynamics\, manifold geometry\, and circuit structure.\n\nAbstract: Single neurons show complex\, heterogeneous responses during cognitive tasks\, often forming low-dimensional manifolds in the population state space. Consequently\, it is widely accepted that neural computations arise from low-dimensional population dynamics while attributing functional properties to individual neurons is impossible. I will present recent work from our lab that bridges single-neuron heterogeneity to manifold geometry and population dynamics. First\, we developed a flexible modeling approach for simultaneously inferring single-trial population dynamics and tuning functions of individual neurons to the latent population state. Applied to spike data recorded during decision-making\, our model revealed that all neurons encode the same dynamic decision variable\, and heterogeneous firing rates result from diverse tuning of single neurons to this decision variable. Second\, using a firing-rate recurrent network model\, we mathematically prove that responses of single neurons cluster into functional types when population dynamics are confined to a low-dimensional linear subspace\, with the number of distinct response types equal to the linear dimension of the neural manifold. We confirm these predictions in recurrent neural networks trained on cognitive tasks and brain-wide neural recordings from mice during a decision-making behavior. Our findings show that low-dimensional population dynamics can be understood in terms of functional cell types\, and random mixed selectivity emerges only in the limit of high-dimensional dynamics.
URL:https://arni-institute.org/event/ctn-tatiana-engel/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241031T110000
DTEND;TZID=America/New_York:20241031T130000
DTSTAMP:20260502T055944
CREATED:20241025T211919Z
LAST-MODIFIED:20241025T211919Z
UID:1116-1730372400-1730379600@arni-institute.org
SUMMARY:CTN: Naureen Ghani
DESCRIPTION:Title: Mice wiggle a wheel to boost the salience of low visual contrast stimuli \n\nAbstract: From the Welsh tidy mouse to the New York City pizza rat\, movement belies rodent intelligence. We show that head-fixed mice develop an active sensing strategy while performing a visual perceptual decision-making task (The International Brain Laboratory\, 2021). Akin to humans shaking a computer mouse to find the cursor on a screen\, we demonstrate that mice wiggle the wheel that controls the movement of a visual stimulus to boost low contrast salience. Moreover\, mice wiggle the wheel at a temporal frequency (11.9 ± 2.9 Hz) optimal for their visual systems (Umino et al\, 2018). With the “old method of watching and wondering about behavior\,” we reveal that mice exploit that it is easier to see something moving than something stationary by wiggling (Tinbergen\, 1973).
URL:https://arni-institute.org/event/ctn-naureen-ghani/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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