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X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
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TZID:America/New_York
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250402T130000
DTEND;TZID=America/New_York:20250402T140000
DTSTAMP:20260424T091735
CREATED:20250324T151641Z
LAST-MODIFIED:20250324T151654Z
UID:1585-1743598800-1743602400@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Continuation of meeting from prior working group meetings. \nZoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250404T150000
DTEND;TZID=America/New_York:20250404T170000
DTSTAMP:20260424T091735
CREATED:20250321T134811Z
LAST-MODIFIED:20250421T155950Z
UID:1581-1743778800-1743786000@arni-institute.org
SUMMARY:ARNI Distinguished Seminar Series: Eftychios A. Pnevmatikakis\, Research Scientist\, Reality labs at Meta
DESCRIPTION:Research Scientist\, Reality labs at Meta \nTitle: TBD \nLocation: TBD \nAbstract: TBD
URL:https://arni-institute.org/event/arni-distinguished-seminar-series-eftychios-a-pnevmatikakis/
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250405T090000
DTEND;TZID=America/New_York:20250405T170000
DTSTAMP:20260424T091735
CREATED:20250312T151528Z
LAST-MODIFIED:20250312T172239Z
UID:1567-1743843600-1743872400@arni-institute.org
SUMMARY:Girls' Science Day
DESCRIPTION:ARNI is committed to promoting science education among New York City’s youth. This year\, ARNI is supporting Girls’ Science Day on April 5\, 2025. \nLocation: TBD \nMission\nGirls’ Science Day at Columbia University seeks to champion the advancement of women and underrepresented groups in the fields of science\, technology\, engineering\, and math (STEM). By offering a full day of hands-on experiments\, we aim to provide middle school girls (5th– 8th grade) with an engaging introduction to science\, spark their curiosity and confidence so they can envision themselves as the next generation of STEM explorers. Purpose Girls’ Science Day is designed to offer participants immersive\, hands-on experiments led by Columbia students. It serves as a lively\, fun\, and accessible entry point into STEM\, providing opportunities for active learning and reflection. \nGoals\n1. Empower Young Scientists: Provide a welcoming space and foster curiosity and excitement about science among middle school girls.\n2. Provide Mentorship: Connect participants with enthusiastic Columbia volunteers — undergraduates\, graduate students\, and postdocs—who can share personal journeys and inspiration.\n3. Strengthen Community Ties: Keep building our local STEM network through close collaboration with parents\, teachers\, and NYC tri-state area schools.
URL:https://arni-institute.org/event/girls-science-day/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250408T150000
DTEND;TZID=America/New_York:20250408T160000
DTSTAMP:20260424T091735
CREATED:20250404T132848Z
LAST-MODIFIED:20250404T132848Z
UID:1602-1744124400-1744128000@arni-institute.org
SUMMARY:ARNI Emerging Researchers Talk Series #1: Rahul Ramesh
DESCRIPTION:Title: Principles of Learning from Multiple Tasks \n\nAbstract: \n\nDeep networks are increasingly trained on data from multiple tasks with the goal of sharing synergistic information across related tasks. A language model\, for example\, is trained on 10 trillion tokens on tasks ranging from programming\, finance\, trivia to translation and a vision model is trained on over a billion images for tasks like object recognition\, depth prediction and semantic segmentation. With this motivation\, in this talk\, I will present the principles behind how to optimally train on multiple tasks and attempt to answer why we are able to learn on these tasks. In the first part of the talk we develop a theory that shows that dissimilar tasks fight for model capacity when trained together. We use this insight to design Model Zoo — a learner that splits its capacity to train many small models on related subsets of tasks — which is state-of-the-art for task-incremental continual learning. In the second half of this talk\, we show that typical tasks are highly redundant functions of the input\, i.e.\, the subspaces that vary the most and ones that vary the least are both highly predictive of typical tasks. This result suggests that there are many subspaces that can be used to solve typical tasks\, which allows us to learn a shared representation for these tasks. We believe that organisms choose to solve redundant tasks because they are the only ones that agents with bounded resources can readily learn. \n\nSpeaker Bio:\nRahul Ramesh is a 6th year PhD student at the University of Pennsylvania in the department of computer and information science and is advised by Pratik Chaudhari. He previously received his B.Tech from the Indian Institute of Technology Madras in Computer science and Engineering. Rahul is interested in using perspectives from statistical learning theory\, information theory and neuroscience to study self-supervised and multitask learning.\n\n\n\nZoom Link: https://columbiauniversity.zoom.us/j/91436346202?pwd=Fa0ohRBhckitrJqVF5gWrUPo5774U2.1
URL:https://arni-institute.org/event/arni-emerging-researchers-talk-series-1-rahul-ramesh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250409T130000
DTEND;TZID=America/New_York:20250409T140000
DTSTAMP:20260424T091735
CREATED:20250324T151844Z
LAST-MODIFIED:20250407T164418Z
UID:1588-1744203600-1744207200@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Guest Speaker: Christopher A. Baldassano
DESCRIPTION:Speaker: Christopher A. Baldassano\n\nTitle: Remembering events using schematic knowledge\nAbstract: Our everyday experiences consist of familiar sequences of events in familiar contexts\, and we use our knowledge of the past to understand and remember the present. Research in my lab combines behavioral\, eye-tracking\, and neuroimaging methods to investigate how prior knowledge of temporal and spatial structure impacts perception and memory\, by allowing participants to draw on their real-world experiences or build detailed expertise in controlled yet naturalistic domains. I’ll discuss our recent studies showing how the brain’s internal cognitive models can be used to organize event perception in narratives\, structure episodic memories\, and anticipate upcoming information. These studies argue for a central role of top-down and anticipatory processes in constructing neural event memories.\n  \nZoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-2/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250409T130000
DTEND;TZID=America/New_York:20250409T140000
DTSTAMP:20260424T091735
CREATED:20250407T134107Z
LAST-MODIFIED:20250421T152301Z
UID:1615-1744203600-1744207200@arni-institute.org
SUMMARY:CTN Special Speaker Steve Fleming
DESCRIPTION:Title: How the human brain thinks about itself\n\nAbstract: The human brain has a remarkable ability to monitor and evaluate its own mental states\, known as metacognition. Metacognition is crucial to success\, enabling us to recognise gaps in our knowledge and collaborate effectively. Problems with metacognition are linked to maladaptive behaviours\, such as endorsing false beliefs or being unaware of our own limitations. In my talk I will review the development of experimental and modelling tools that allow us to isolate how metacognitive capacity relates to human brain function and supports a rich awareness of our skills and capabilities. I will explore the psychological structure of metacognition across different tasks and cognitive domains\, and ask how self-evaluative judgment contributes to belief formation and changes of mind. I’ll end by considering the implications of a science of metacognition for mental health\, education and AI.\n\nZoom link: https://columbiauniversity.zoom.us/j/93699792071?pwd=FMzvmSSLhb8mbibdk05s72eFoRRpVh.1
URL:https://arni-institute.org/event/cnt-special-speaker-steve-fleming/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250411T113000
DTEND;TZID=America/New_York:20250411T130000
DTSTAMP:20260424T091735
CREATED:20250407T134230Z
LAST-MODIFIED:20250421T152247Z
UID:1618-1744371000-1744376400@arni-institute.org
SUMMARY:CTN: Alex Williams
DESCRIPTION:Title: Quantifying individuality in neural circuit representations\n \nAbstract: Signatures of neural computation are thought to be reflected in the coordinated activity of large neural populations. Neuroscience is now flush with measurements of these activity patterns in humans\, animal subjects\, and large-scale artificial network models. In this talk\, I will address an extensively studied\, yet unresolved\, question: How should we quantify the extent to which two or more neural circuits have “similar” activation patterns? Without an answer to this question\, the field has struggled to investigate basic questions about biological variability and individuality\, such as: How do neural representations vary across a healthy population? How do differences in neural population activity correlate with behavioral idiosyncrasies and disorders? How similar are computational mechanisms in biological brains and artificial neural networks? In this talk\, I will summarize several mathematical methods that quantify similarity in neural representations and demonstrate how they provide early insights into these questions when applied to biological data and artificial networks.\n\nZoom Link: https://columbiauniversity.zoom.us/j/93699792071?pwd=FMzvmSSLhb8mbibdk05s72eFoRRpVh.1
URL:https://arni-institute.org/event/cnt-alex-williams/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250416T130000
DTEND;TZID=America/New_York:20250416T140000
DTSTAMP:20260424T091735
CREATED:20250324T151937Z
LAST-MODIFIED:20250415T142931Z
UID:1590-1744808400-1744812000@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:LLM Benchmarks \nZoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-3/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250418T113000
DTEND;TZID=America/New_York:20250418T130000
DTSTAMP:20260424T091735
CREATED:20250407T134745Z
LAST-MODIFIED:20250421T152319Z
UID:1622-1744975800-1744981200@arni-institute.org
SUMMARY:CTN: Sam Gershman
DESCRIPTION:Title: Reimagining the biology of memory\n \nAbstract: Over the last half century\, there has been a remarkable convergence on the idea that memories are stored at synapses. I will argue that this is only part of the story. A more complete story compels us to recognize the radical ubiquity of memory in living systems\, including free-living unicellular organisms and many kinds of non-neural cells. Memory existed from the moment life began; in a sense it is built into the logic of life. Its molecular mechanisms are therefore likely to be ancient in origin\, and a number of clues are already available. Computational considerations help us organize these clues into a theory of the division of labor and interaction between cell-intrinsic and synaptic storage mechanisms. From this new starting point\, I will explore how we can make sense of many strange and puzzling phenomena: the transfer of memory between organisms\, the survival of memory after radical synaptic remodeling (even decapitation!)\, the transience of amnesia following protein synthesis inhibition\, and the ability of unicellular organism to learn\, among others.\nZoom Link: https://columbiauniversity.zoom.us/j/91727702242?pwd=FFy1WpaEG63QKbRgaNsueWdOy4kQpP.1
URL:https://arni-institute.org/event/cnt-sam-gershman/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250422T150000
DTEND;TZID=America/New_York:20250422T160000
DTSTAMP:20260424T091735
CREATED:20250404T133014Z
LAST-MODIFIED:20250421T150836Z
UID:1606-1745334000-1745337600@arni-institute.org
SUMMARY:ARNI Emerging Researchers Talk Series #2: Itzel Olivos-Castillo
DESCRIPTION:Bio: Itzel is a Ph.D. student at Rice University working with Prof. Xaq Pitkow. She studies perception and control mechanisms that give biological organisms an advantage over machines. She believes understanding how the brain works using mathematical principles is essential to build the next generation of AI systems which are more robust\, more general-purpose\, less artificial\, and more intelligent. She holds a bachelor’s degree (Telematics Engineering) and master’s degree (Computer Science) from Instituto Politécnico Nacional (IPN-Mexico). \nTitle: Resource-Efficient Control in Brains and Machines \nAbstract:\nThe brain can turn noisy stimuli into rational behaviors that address a wide variety of tasks using limited\nexperience\, relying on limited processing capacity\, and consuming less energy than a lightbulb. What makes the\nbrain such an efficient control system? Cognitive studies have identified meta-reasoning\, the ability to reason\nabout one’s own reasoning process\, as a crucial factor behind this remarkable performance. However\, it remains\nunclear how meta-level rational agents—whether biological or artificial—successfully balance internal\ncomputation costs against task performance in uncertain environments. To help bridge this gap\, we develop a\nnovel approach to stochastic control where the internal computation cost of inference (a resource-intensive\nmechanism that aids in mitigating uncertainty) is optimized alongside task performance. We apply our framework\nto quantitatively examine how meta-level rational agents solve Linear Quadratic Gaussian problems. Our findings\nreveal that when the estimation error is a meta-control variable the agent can regulate\, the dynamics of inference\nand control become tightly coupled. This coupling leads to intriguing phase transitions in what is worth\nrepresenting\, switching from a costly but maximally informative strategy to a family of solutions that differ in\nhow the agent integrates new evidence\, corrects estimation errors\, and models the world to lessen the burden of\noptimal inference. The fundamental principles we found generalize efficient coding ideas\, extend the principle of\nminimal intervention in control\, and provide a foundation for a new type of rational behavior that both brains and\nmachines could use for effective but computationally constrained control. \nZoom Link: https://columbiauniversity.zoom.us/j/91436346202?pwd=Fa0ohRBhckitrJqVF5gWrUPo5774U2.1
URL:https://arni-institute.org/event/arni-emerging-researchers-talk-series-2-kathrine-xu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250423T113000
DTEND;TZID=America/New_York:20250423T130000
DTSTAMP:20260424T091735
CREATED:20250421T152454Z
LAST-MODIFIED:20250421T152454Z
UID:1667-1745407800-1745413200@arni-institute.org
SUMMARY:CTN: Ivan Davidovich
DESCRIPTION:Title: Uncovering latent low-dimensional structure in network connectivity \nAbstract: Network connectivity constrains the patterns of neural activity in the brain. These constraints are often observed as low-dimensional manifolds in neural activity space. Continuous Attractor Networks (CANs) are a prime example of this type of network phenomenon. Interestingly\, there are examples of CANs where the structure or topology of the manifold observed in the space of neural activity does not match the corresponding structure or topology of the connection weights in the network. To learn more about this relationship\, we need to go beyond studying the structure of neural activity and investigate the structure in the connectivity of those systems. To this end\, we wish to identify a minimal set of parameters\, or coordinates\, that are enough to characterize the connectivity weights between any pair of neurons given their coordinate values. In the simplest cases\, this is equivalent to finding an appropriate ordering (labelling) of cells that will reveal the underlying structure in the connectivity weights. Traditional approaches use properties of neural activity\, such as neural selectivity\, to identify such an ordering. However\, there are many situations that are not amenable to this treatment\, either because neural activity data is not available\, for example in connectome data sets\, because tuning curves are disordered\, or because of the particular architecture of the network. To address this issue\, we employ tools from Dimensionality Reduction and Topological Data Analysis to uncover structure directly from the connectivity weights in different examples of CAN models. I will show that this approach can uncover connectivity structure that is different from the one observed in activity space\, and in some cases works even when a fairly large fraction of neurons in the system is not observed. We argue that this perspective towards the study of structure in network connectivity can lead to the discovery of organization in cases where no obvious structure is present in the activity of the neural population\, or where connectomics data is available without corresponding activity recordings.
URL:https://arni-institute.org/event/ctn-ivan-davidovich/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250423T130000
DTEND;TZID=America/New_York:20250423T140000
DTSTAMP:20260424T091735
CREATED:20250324T152024Z
LAST-MODIFIED:20250324T152159Z
UID:1592-1745413200-1745416800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Zoom link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-4/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250425T113000
DTEND;TZID=America/New_York:20250425T130000
DTSTAMP:20260424T091735
CREATED:20250421T152632Z
LAST-MODIFIED:20250421T155923Z
UID:1670-1745580600-1745586000@arni-institute.org
SUMMARY:CTN: Inês Laranjeira
DESCRIPTION:Title: The structure of individuality in micro-behavioral features of task performance \nAbstract: Individuality is an intrinsic and essential aspect of mammalian behavior that emerges even in genetically identical organisms experiencing the same environmental conditions. In the International Brain Laboratory (IBL)\,  mice were trained on a visual decision-making task with the explicit goal of establishing a rigorously standardized experimental protocol. This effort led to an automated pipeline that produced trained mice whose behavior was indistinguishable across seven different labs\, when considering trial-level descriptors of behavior. Nevertheless\, substantial inter-individual variability was evident in both training time and proficient behavior\, but its nature remains poorly characterized. To address this\, we developed a behavioral segmentation approach to characterize mouse behavior across multiple variables. This yielded a discrete space of behavioral syllables which we further analyzed in the context of the trial structure. Variability in the expression of behavioral syllables was highly non-random\, revealing structure in how different behavioral features co-vary at the sub-trial level. Moreover there was further evidence that mice fell into several clusters\, suggestive of strategy types or even mouse personality types. The micro-behavioral structure derived from trained mice was further informative of differences in learning speed across individual mice\, supporting its stability and biological significance. Overall\, these results provide evidence that even in a cohort of mice whose overt task performance behavior is indistinguishable\, there exist latent variables\, manifesting in the details of micro-behavioral features\, which appear to explain important aspects of behavioral individuality.
URL:https://arni-institute.org/event/ctn-ines-laranjeira/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250425T120000
DTEND;TZID=America/New_York:20250425T170000
DTSTAMP:20260424T091735
CREATED:20250312T143025Z
LAST-MODIFIED:20250404T155414Z
UID:1565-1745582400-1745600400@arni-institute.org
SUMMARY:ARNI Emerging Researchers Symposium
DESCRIPTION:This event is for all ARNI trainees and graduate students who work on an ARNI related project. The goal of this symposium is to foster networking and career development. \nLocation: Zuckerman Institute L3-079\nTime: 12pm to 5pm\nRegistration form: https://forms.gle/4e2AHqP54X8VvkKS8 \n\n\n\nProgram \n\n\n\n\n12pm: Lunch \n\n\nCatering by FUMO\n\n\n\n\n1pm: ARNI Postdoc Presentations \n\n\nHaozhe Shan\nMehdi Azabou\n\n\n\n\n2pm: Postdoc Panel Session \n\nAcademic Research and Entrepreneurship\n\n2:45pm – 3:15pm: Break \n\n\n\n\n3:15pm: Academic Speed Dating \n\n\n\n4pm: Large Group Discussion \n\n5-6 important but challenging and controversial topics in understanding the principle of intelligence and NeuroAI
URL:https://arni-institute.org/event/arni-emerging-researchers-symposium/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260424T091735
CREATED:20250414T170342Z
LAST-MODIFIED:20250422T205048Z
UID:1644-1745924400-1745928000@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:The working group will discuss: https://www.nature.com/articles/s41593-020-0671-1 \nJoin via Google Meets: meet.google.com/nnq-csiy-yah
URL:https://arni-institute.org/event/arni-biological-learning-working-group-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260424T091735
CREATED:20250423T201602Z
LAST-MODIFIED:20250423T201627Z
UID:1678-1745924400-1745928000@arni-institute.org
SUMMARY:Lecture in AI: Eric Xing
DESCRIPTION:Columbia Engineering Lecture Series in AI\n“Toward General and Purposeful Reasoning in Real World Beyond Lingual Intelligence“\nApril 29: Dr. Eric Xing\, President of Mohammed bin Zayed University of Artificial Intelligence \nRegister here! \nSchedule \n\n10:30AM-11:00AM Registration\n11:00AM-12:00PM Lecture\n\nAdvance registration is required for both Columbia affiliates and non-affiliates \nABOUT THE SPEAKER \nProfessor Xing is the inaugural president of MBZUAI\, where he has led the university’s remarkable growth in AI research and assembled a world-class faculty. Under his leadership\, MBZUAI has developed a platform for students and faculty to advance research aligned with national priorities\, balancing excellence in both fundamental research and translational R&D. His vision has fostered numerous high-profile partnerships with institutions like IBM\, Carnegie Mellon University\, and the Weizmann Institute of Science\, while also creating specialized training programs for senior executives and government leaders. He has also overseen the development of state-of-the-art facilities\, including a supercomputing center optimized for AI research. \nA world-renowned computer scientist\, Professor Xing has made pioneering contributions to statistical machine learning\, including innovations in distance metric learning\, network analysis\, and distributed machine learning systems. His research spans core machine learning\, large-scale system architecture\, healthcare applications\, and computational biology. He is a champion for open-source AI\, having founded the CASL project to standardize AI operating systems for better scalability and industry integration. Professor Xing is a recipient of numerous prestigious awards\, including the NSF Career Award and the Alfred P. Sloan Research Fellowship. He is a fellow of multiple esteemed organizations\, including the AAAI\, IEEE\, and ACM.
URL:https://arni-institute.org/event/lecture-in-ai-eric-xing/
LOCATION:Davis Auditorium\, 530 W 120th St\, New York\, NY 10027\, New York\, NY\, 10027
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250430T140000
DTEND;TZID=America/New_York:20250430T160000
DTSTAMP:20260424T091735
CREATED:20250128T200004Z
LAST-MODIFIED:20250414T163102Z
UID:1479-1746021600-1746028800@arni-institute.org
SUMMARY:ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior)
DESCRIPTION:Description: Advances in neurotechnology and behavioral tracking have enabled the collection of large-scale neural and behavioral datasets\, offering new opportunities to study brain function in complex settings. However\, researchers face significant challenges in integrating and analyzing data across different brain regions\, individuals\, and behavioral contexts. Inspired by recent successes in large-scale “foundation” models in natural language and computational biology\, there is growing interest in developing both neurofoundation models—which learn from diverse neural recordings—and behavior foundation models—which capture structure in high-dimensional behavioral data. These models can be used for downstream tasks such as neural encoding and decoding\, cell-type classification\, and behavioral prediction. The goal of this working group is to explore this emerging field\, address challenges in scaling these models\, identify needs for benchmarks and tools to accelerate progress\, and determine how these models can be used to answer scientific questions in both neuroscience and behavioral research. \nMeeting Description: Our inaugural meeting will begin with a short talk summarizing the landscape of foundation models for neuroscience and behavior\, highlighting recent advances\, key challenges\, and opportunities for improvement. This will be followed by an open discussion to define the group’s collaborative focus and shared priorities. \nZoom Link: https://columbiauniversity.zoom.us/j/95445157877?pwd=FawUA2r6I1bxMN3W2uyok4YtF6QdpZ.1
URL:https://arni-institute.org/event/arni-animal-behavior-working-group-meeting/
LOCATION:Zuckerman Institute – L7-119\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
END:VCALENDAR