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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260403T141720
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:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260403T141720
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:20250425T120000
DTEND;TZID=America/New_York:20250425T170000
DTSTAMP:20260403T141720
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:20250425T113000
DTEND;TZID=America/New_York:20250425T130000
DTSTAMP:20260403T141720
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:20250423T130000
DTEND;TZID=America/New_York:20250423T140000
DTSTAMP:20260403T141720
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:20250423T113000
DTEND;TZID=America/New_York:20250423T130000
DTSTAMP:20260403T141720
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:20250422T150000
DTEND;TZID=America/New_York:20250422T160000
DTSTAMP:20260403T141720
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:20250418T113000
DTEND;TZID=America/New_York:20250418T130000
DTSTAMP:20260403T141720
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:20250416T130000
DTEND;TZID=America/New_York:20250416T140000
DTSTAMP:20260403T141720
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:20250411T113000
DTEND;TZID=America/New_York:20250411T130000
DTSTAMP:20260403T141720
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:20250409T130000
DTEND;TZID=America/New_York:20250409T140000
DTSTAMP:20260403T141720
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:20250409T130000
DTEND;TZID=America/New_York:20250409T140000
DTSTAMP:20260403T141720
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:20250408T150000
DTEND;TZID=America/New_York:20250408T160000
DTSTAMP:20260403T141720
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:20250405T090000
DTEND;TZID=America/New_York:20250405T170000
DTSTAMP:20260403T141720
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:20250404T150000
DTEND;TZID=America/New_York:20250404T170000
DTSTAMP:20260403T141720
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:20250402T130000
DTEND;TZID=America/New_York:20250402T140000
DTSTAMP:20260403T141720
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:20250321T110000
DTEND;TZID=America/New_York:20250321T130000
DTSTAMP:20260403T141720
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250319T113000
DTEND;TZID=America/New_York:20250319T130000
DTSTAMP:20260403T141720
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:20250314T113000
DTEND;TZID=America/New_York:20250314T130000
DTSTAMP:20260403T141720
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:20250311T160000
DTEND;TZID=America/New_York:20250311T170000
DTSTAMP:20260403T141720
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250307T113000
DTEND;TZID=America/New_York:20250307T130000
DTSTAMP:20260403T141720
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250304T090000
DTEND;TZID=America/New_York:20250304T170000
DTSTAMP:20260403T141720
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:20250228T153000
DTEND;TZID=America/New_York:20250228T170000
DTSTAMP:20260403T141720
CREATED:20250128T194129Z
LAST-MODIFIED:20250221T200454Z
UID:1474-1740756600-1740762000@arni-institute.org
SUMMARY:ARNI Distinguished Seminar Series: Marlene Behrmann
DESCRIPTION:About Dr. Marlene Behrmann:\nMarlene Behrmann joined the Department of Ophthalmology at the University of Pittsburgh School of Medicine\, where she holds the John and Clelia Sheppard Chair\, in 2022. She also holds the position of Emeritus Professor at Carnegie Mellon University. Dr. Behrmann’s research is concerned with the psychological and neural bases of visual processing\, with specific attention to the mechanisms by which the signals from the eye are transformed into meaningful percepts by the brain. She adopts an interdisciplinary approach combining computational\, neuropsychological and neuroimaging studies with adults and children in health and disease. Examples of her recent studies include investigations of the cortical visual system in paediatric patients following hemispherectomy and identifying mechanisms of plasticity and elucidating the potential for cortical reorganization\, but she has also studied visual cortical function in individuals with inherited retinal dystrophy. Dr. Behrmann was elected a member of the Society for Experimental Psychologists in 2008\, and was inducted into the National Academy of Sciences in 2015\, and into the American Academy of Arts and Sciences in 2019. Dr Behrmann has received many awards including the Presidential Early Career Award for Engineering and Science\, the APA Distinguished Scientific Award for Early Career Contributions and the Fred Kavli Distinguished Career Contributions in Cognitive Neuroscience Award from the Cognitive Neuroscience Society. \nTitle: The development\, hemispheric organization\, and plasticity of high-level vision \nAbstract: \nAdults recognize complex visual inputs\, such as faces and words\, with remarkable speed\, accuracy and ease\, but a full understanding of these abilities is still lacking. Much prior research has favoured a binary separation of faces and words\, with the right hemisphere specialized for the representation of faces\, and the left hemisphere specialized for the representation of words. Close scrutiny of the data\, however\, suggest a more graded and distributed hemispheric organization\, as well as differing hemispheric profiles across individuals. Combining detailed behavioral data with structural and functional imaging data reveals how the distribution of function both within and between the two cerebral hemispheres emerges over the course of development\, and a computational account of this mature organization is offered and tested. Provocatively\, this mature profile is more malleable than previously thought\, and cross-sectional and longitudinal data acquired from individuals with hemispherectomy reveal how a single hemisphere can subserve both visual classes. Together\, the findings support a view of cortical visual organization (and perhaps\, the organization of other functions too) as plastic and dynamic\, both within and between hemispheres. \nLocation: Zuckerman Institute\, Kavli Auditorium 9th Floor (for access to Zuckerman Institute\, please email Lena Mei @ lm3440@columbia.edu 24 hours prior to the event) \nZoom link: https://columbiauniversity.zoom.us/j/96156119664?pwd=PCGPe1UbEzzbIvGnbAdVa8wX5wH9J0.1
URL:https://arni-institute.org/event/arni-distinguished-seminar-series-marlene-behrmann/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250227T130000
DTEND;TZID=America/New_York:20250227T140000
DTSTAMP:20260403T141720
CREATED:20250221T155402Z
LAST-MODIFIED:20250221T165304Z
UID:1511-1740661200-1740664800@arni-institute.org
SUMMARY:ARNI Continual Learning Project
DESCRIPTION:Followup to discussion in Meeting 1 \nZoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1 \n  \n  \n 
URL:https://arni-institute.org/event/arni-continual-learning-project/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250226T140000
DTEND;TZID=America/New_York:20250226T150000
DTSTAMP:20260403T141720
CREATED:20250218T211553Z
LAST-MODIFIED:20250218T211606Z
UID:1501-1740578400-1740582000@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Ken Miller will be talking about E/I networks & balanced networks and some computational/functional implications\, there’s two papers I’d suggest reading:on balanced amplification: https://www.sciencedirect.com/science/article/pii/S0896627309001287 review of loosely and tightly balanced networks: https://www.sciencedirect.com/science/article/pii/S0896627321005754. \n\nMeeting Link: meet.google.com/nnq-csiy-yah
URL:https://arni-institute.org/event/arni-biological-learning-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250225T150000
DTEND;TZID=America/New_York:20250225T170000
DTSTAMP:20260403T141720
CREATED:20250217T144605Z
LAST-MODIFIED:20250224T195355Z
UID:1490-1740495600-1740502800@arni-institute.org
SUMMARY:ARNI WG Multi-resource-cost optimization of neural network models: Paul Schrater
DESCRIPTION:Title: Control when confidence is costly \nAbstract:\nWe develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control\, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically\, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance\, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands\, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations\, each misestimate the stability of the world. In all cases\, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control.\nWe develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control\, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically\, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance\, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands\, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations\, each misestimate the stability of the world. In all cases\, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control. \nZoom Link: https://columbiauniversity.zoom.us/j/98244449046?pwd=ZagtGamVQgwy8XrPdXdlzJRbgrXtVj.1
URL:https://arni-institute.org/event/arni-wg-multi-resource-cost-optimization-of-neural-network-models-paul-schrater/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250218T160000
DTEND;TZID=America/New_York:20250218T164500
DTSTAMP:20260403T141720
CREATED:20250217T150602Z
LAST-MODIFIED:20250217T150623Z
UID:1493-1739894400-1739897100@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Spring Opening Meeting
DESCRIPTION:From: Tom Zollo\n\n\n\n\n\nIn Y2\, the aim is to use this working group as a launchpad for a larger ARNI continual learning project (which we hope to spawn multiple subprojects and papers).  We hope for this group to tackle issues that are relevant to both modern practitioners and the ARNI mission of connecting artificial and natural intelligence.\n\nAs a potential topic for this project\, we think we might consider the problem of long and short term memory in LLMs.  There has been recent interest from industry labs\, e.g. Google (paper link) and Meta (paper link)\, in fitting an LLM with a long-term neural memory module to complement the short-term memory given by the context window.  Several threads relevant to ARNI could extend from this research direction.  For instance\, we might consider cognitively-inspired benchmarks for LLM memory systems for lifelong learning\, e.g.\, based on human-like tasks that might be difficult for autoregressive models.  Also\, we could explore methodological work in LLM memory mechanisms based on our understanding of natural intelligence.  We are particularly interested in learning about relevant studies in neuroscience and cognitive science that could help constrain and inspire the methodological approaches.  Beyond these\, one could imagine many other related directions of interest to ARNI.\n\n\nZoom: https://columbiauniversity.zoom.us/j/99160043324?pwd=1BvBZBeyB3b8da74wuLsgPCabCVudL.1
URL:https://arni-institute.org/event/arni-continual-learning-working-group-spring-opening-meeting/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250210T113000
DTEND;TZID=America/New_York:20250210T130000
DTSTAMP:20260403T141720
CREATED:20250127T152702Z
LAST-MODIFIED:20250127T152702Z
UID:1463-1739187000-1739192400@arni-institute.org
SUMMARY:CTN Monda Lab: Liam Paninski
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-monda-lab-liam-paninski/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250207T113000
DTEND;TZID=America/New_York:20250207T130000
DTSTAMP:20260403T141720
CREATED:20250127T152415Z
LAST-MODIFIED:20250127T152743Z
UID:1460-1738927800-1738933200@arni-institute.org
SUMMARY:CTN: Eva Naumann
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-eva-naumann/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250205T113000
DTEND;TZID=America/New_York:20250205T130000
DTSTAMP:20260403T141720
CREATED:20250127T152236Z
LAST-MODIFIED:20250127T152236Z
UID:1456-1738755000-1738760400@arni-institute.org
SUMMARY:CTN: Hidenori Tanaka
DESCRIPTION:Hidenori Tanaka \nTitle and Abstract: TBD
URL:https://arni-institute.org/event/ctn-hidenori-tanaka/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
END:VCALENDAR