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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260123T120000
DTEND;TZID=America/New_York:20260123T130000
DTSTAMP:20260403T154252
CREATED:20260113T193600Z
LAST-MODIFIED:20260113T193600Z
UID:2194-1769169600-1769173200@arni-institute.org
SUMMARY:Language and Vision Working Group
DESCRIPTION:Initial Meeting! \nAbout: \nThe ARNI Language & Vision Working Group aims to bring together researchers across neuroscience\, cognitive science\, computer science\, and AI to collaboratively advance our understanding of how humans and machines construct multimodal experiences. Its goal is to create a space for discussing ongoing language- and vision-focused projects\, identifying natural points of overlap\, and transforming them into larger\, interdisciplinary initiatives. Grounded in the idea that language and vision form a dynamic\, symbiotic system rather than isolated modules\, the group seeks to explore how this integration is represented in the brain and in the machine. Strengthening collaboration between these domains is essential for building the next generation of AI systems that learn from continual\, multimodal input\, reflect human cognitive principles\, and ultimately support real-world human needs. \nMore questions: Contact Anna Krason (akrason@gc.cuny.edu) \nZoom: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/language-and-vision-working-group/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260123T113000
DTEND;TZID=America/New_York:20260123T130000
DTSTAMP:20260403T154252
CREATED:20260113T192529Z
LAST-MODIFIED:20260121T163725Z
UID:2193-1769167800-1769173200@arni-institute.org
SUMMARY:CTN: Scott Linderman
DESCRIPTION:Title: When and How to Parallelize Seemingly Sequential Models\n \nAbstract: Transformers have become the de facto model for sequential data in large part because they are well adapted to modern hardware: At training time\, the loss can be evaluated in parallel over the sequence length on GPUs and TPUs. By contrast\, evaluating nonlinear recurrent neural networks (RNNs) appears to be an inherently sequential problem. However\, recent advances like DEER (arXiv:2309.12252) and DeepPCR (arXiv:2309.16318) have shown that evaluating a nonlinear recursion can be recast as solving a parallelizable optimization problem\, and sometimes this approach can yield dramatic speed-ups in wall-clock time. However\, the factors that govern the difficulty of these optimization problems remain unclear\, limiting the larger adoption of the technique. I will present a recent line of work from my lab that further develops these methods in both theory and practice. We establish a precise relationship between the dynamics of a nonlinear system and the conditioning of its corresponding optimization formulation. We show that the predictability of a system\, defined as the degree to which small perturbations in state influence future behavior\, impacts the number of optimization steps required for evaluation. In predictable systems\, the state trajectory can be computed in O(log2T) time\, where T is the sequence length\, a major improvement over the conventional sequential approach. In contrast\, chaotic or unpredictable systems exhibit poor conditioning\, with the consequence that parallel evaluation converges too slowly to be useful. We validate our claims through extensive experiments\, with a particular emphasis on parallelizing nonlinear RNNs and Markov chain Monte Carlo (MCMC) algorithms for Bayesian statistics. I will provide practical guidance on when nonlinear dynamical systems can be efficiently parallelized\, and highlighting predictability as a key design principle for parallelizable models.
URL:https://arni-institute.org/event/ctn-scott-linderman/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260122T150000
DTEND;TZID=America/New_York:20260122T153000
DTSTAMP:20260403T154252
CREATED:20260121T163934Z
LAST-MODIFIED:20260121T163934Z
UID:2248-1769094000-1769095800@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:To kick off the Continual Learning Working Group activities for this year\, the Continual Learning working group will meet on Thursday\, Jan 22 at 3pm on Zoom and in CEPSR 620.\n\nThe meeting will be brief\, and we’ll discuss our agenda and scheduling for the coming semester\, including our goals for the benchmark project.\nJoin via Zoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/continual-learning-working-group-11/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260120T150000
DTEND;TZID=America/New_York:20260120T160000
DTSTAMP:20260403T154252
CREATED:20251211T202316Z
LAST-MODIFIED:20260113T180504Z
UID:2162-1768921200-1768924800@arni-institute.org
SUMMARY:Speaker: Xaq Pitkow ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Title: Frugal Inference for Control\n\nAbstract: A key challenge in advancing artificial intelligence is achieving the right balance between utility maximization and resource use by both external movement and internal computation. While this trade-off has been studied in fully observable settings\, our understanding of resource efficiency in partially observable environments remains limited. Motivated by this challenge\, we develop a version of the POMDP framework where the information gained through inference is treated as a resource that must be optimized alongside task performance and motion effort. By solving this problem in environments described by linear-Gaussian dynamics\, we uncover fundamental principles of resource efficiency. Our study reveals a phase transition in the inference\, switching from a Bayes-optimal approach to one that strategically leaves some uncertainty unresolved. This frugal behavior gives rise to a structured family of equally effective strategies\, facilitating adaptation to later objectives and constraints overlooked during the original optimization. We illustrate the applicability of our framework and the generality of the principles we derived using two nonlinear tasks. Overall\, this work provides a foundation for a new type of rational computation that both brains and machines could use for effective but resource-efficient control under uncertainty.\n\nZoom Link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-xaq-pitkow-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260116T113000
DTEND;TZID=America/New_York:20260116T130000
DTSTAMP:20260403T154252
CREATED:20260113T180942Z
LAST-MODIFIED:20260113T180942Z
UID:2191-1768563000-1768568400@arni-institute.org
SUMMARY:CTN: Nao Uchida
DESCRIPTION:Title: A normative perspective on diversity of dopamine neurons \nAbstract: TBD
URL:https://arni-institute.org/event/ctn-nao-uchida/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251219T113000
DTEND;TZID=America/New_York:20251219T130000
DTSTAMP:20260403T154252
CREATED:20251209T200448Z
LAST-MODIFIED:20251216T170437Z
UID:2159-1766143800-1766149200@arni-institute.org
SUMMARY:CTN: Roozbeh Kiani
DESCRIPTION:Seminar Time: 11:30am\nDate: 12/19/25\nSeminar Location: JLG\, L5-084\nHost: Tahereh Toosi\n\n \n\nTitle: Flexible decision-making: policies and rules\n \nAbstract: Flexible behavior requires flexible decision-making. We adapt seamlessly to changing environments—adjusting biases\, altering decision rules\, and inferring hidden task contexts—often without explicit cues. In this talk\, I will outline a framework that formalizes different levels of this flexibility and show how these adjustments are implemented in neural codes across the frontoparietal cortex. I will highlight three forms of decision flexibility: (1) Bias adjustments\, driven by asymmetric rewards\, shift neural activity along the decision variable axis;  (2) Rule changes\, such as varying sensory weights in a multi-feature discrimination task\, produce rotational changes in the population geometry\, supporting rapid changes in decision policy; and (3) Hierarchical inference\, where animals infer hidden contexts to adapt to task structure\, is reflected in the emergence of latent variables represented in distributed subspaces.
URL:https://arni-institute.org/event/ctn-roozbeh-kiani/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251212T113000
DTEND;TZID=America/New_York:20251212T130000
DTSTAMP:20260403T154252
CREATED:20251209T200320Z
LAST-MODIFIED:20251209T200320Z
UID:2157-1765539000-1765544400@arni-institute.org
SUMMARY:CTN: Mehrdad Jazayeri
DESCRIPTION:Title: Adaptive problem solving in the primate frontal cortex\n\nAbstract: Humans excel at solving problems adaptively. When missing the bus to an appointment\, for instance\, we might wait for the next one\, call a taxi\, cancel\, or reschedule\, depending on the situation. This ability to assess context and choose a suitable strategy is central to intelligence\, yet its neural and computational foundations remain poorly understood. To address this gap\, we trained monkeys on a challenging decision-making task that could be solved using multiple strategies\, providing a controlled setting to study strategic flexibility. Behaviorally\, the animals performed accurately and generalized to new conditions\, but their choices were inconsistent with any single policy\, suggesting the use of internally generated strategies. Large-scale electrophysiological recordings from the dorsomedial frontal cortex revealed that population activity unfolded along distinct neural trajectories corresponding to different strategies. The structure of these trajectories—set by the organization of initial neural states and their subsequent evolution—showed that animals assessed the problem and engaged distinct\, rationally structured computational algorithms. A latent behavioral model grounded in these neural dynamics predicted the animals’ choices more accurately than any fixed-strategy model\, providing a direct link between cortical population activity and adaptive decision-making. Together\, these findings reveal a neurophysiological mechanism for strategic decision-making and offer a mechanistic understanding of the neural basis of adaptive problem solving.
URL:https://arni-institute.org/event/ctn-mehrdad-jazayeri/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251211T150000
DTEND;TZID=America/New_York:20251211T160000
DTSTAMP:20260403T154252
CREATED:20251209T181210Z
LAST-MODIFIED:20251209T181210Z
UID:2095-1765465200-1765468800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Next Meeting Info\n\n\nDate: Thursday\, Dec 11\nTime: 3pm-4pm\nRoom: CEPSR 620\nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-4/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251210T110000
DTEND;TZID=America/New_York:20251210T120000
DTSTAMP:20260403T154252
CREATED:20251125T161729Z
LAST-MODIFIED:20251201T155241Z
UID:2039-1765364400-1765368000@arni-institute.org
SUMMARY:Speaker: Alan Stocker ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Alan Stocker\nProfessor of Psychology at UPenn \nTitle: Economics of temporal evidence integration \nAbstract: The temporal integration of sensory information is an important aspect of many human decision tasks. I will present results of ongoing research in my laboratory aimed at understanding the dynamic processes underlying evidence integration. In particular\, I will discuss a novel resource-rational model that treats both the representation as well as the integration and maintenance of sensory evidence as actively controlled\, performance-effort trade-off mechanisms. Validated against data from various behavioral experiments\, the model not only provides a normative explanation for observed non-linear dynamics in evidence integration but also a parsimonious explanation for individual tendencies for recency or primacy behavior. As the work is ongoing and unpublished\, I am looking forward to an engaged discussion with the audience. \nZoom Link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-alan-stocker-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251121T113000
DTEND;TZID=America/New_York:20251121T123000
DTSTAMP:20260403T154252
CREATED:20251105T152923Z
LAST-MODIFIED:20251118T184741Z
UID:2030-1763724600-1763728200@arni-institute.org
SUMMARY:CTN: Karel Svoboda
DESCRIPTION:Seminar Time: 11:30am\nDate: 11/21/25\nSeminar Location: JLG\, L5-084\nHost: Ji Xia\n\n\nTitle: Illuminating synaptic learning \nAbstract: How do synapses in the middle of the brain know how to adjust their weight to advance a behavioral goal? This is referred to as the synaptic ‘credit assignment problem’. A large variety of synaptic learning rules have been proposed\, mainly in the context of artificial neural networks. The most powerful learning rules (e.g. back-propagation of error) are thought to be biologically implausible\, whereas the widely studied biological learning rules (Hebbian) are insufficient for goal-directed learning. I will describe ongoing work\, both experimental and theoretical\, focused on understanding learning at the level of circuits and synapses in the motor cortex. 
URL:https://arni-institute.org/event/ctn-karel-svoboda/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251117T080000
DTEND;TZID=America/New_York:20251118T130000
DTSTAMP:20260403T154252
CREATED:20251112T151814Z
LAST-MODIFIED:20251112T151821Z
UID:2033-1763366400-1763470800@arni-institute.org
SUMMARY:ARNI Annual Retreat 2025
DESCRIPTION:To celebrate the many accomplishments as we wrap up year two and continue our momentum into year three. \nWe anticipate engaging discussions in the working groups and panels as we explore future directions for ARNI. \nWe also want to highlight the participation of Bing Brunton\, Jim DiCarlo\, and Thomas Reardon from our External Advisory Board. \nBy registration only!
URL:https://arni-institute.org/event/arni-annual-retreat-2025/
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T143000
DTEND;TZID=America/New_York:20251113T153000
DTSTAMP:20260403T154252
CREATED:20251103T193526Z
LAST-MODIFIED:20251103T193526Z
UID:2025-1763044200-1763047800@arni-institute.org
SUMMARY:Carl Vondick Hosts Talk with Aaron Hertzmann (Adobe)
DESCRIPTION:Aaron Hertzmann \nWhy Do Pictures Work? Explanations From Real-World Vision\nSpeaker: Aaron Hertzmann (Adobe)\nHost: Carl Vondrick\nDate: Thursday\, November 13\, 2025\nTime: 2:30 PM\nLocation: CSB 453\n\nAbstract: I outline possible answers to the long-standing question of why pictures work: why can people look at a painting or photograph\, and see a depicted subject\, rather than just marks on a page or lights on a display? Observers with no prior experience with pictures can understand some kinds of pictures\, indicating that picture understanding is not solely a product of experience or culture.  I argues that picture perception can be explained as a product of several properties of real-world vision. First\, the fact that humans can understand certain real-world phenomena—refraction\, reflection\, cast shadows—as simultaneously surface phenomena but also images of an underlying cause explains why we can see pictures as depictions and not just markings.  Second\, the fact that viewers can understand real-world scenes with unfamiliar combinations of objects explains our ability to understand many different styles of depiction. For example\, we can understand black-and-white photos of people because\, in real-world vision\, we could recognize a familiar person who had been painted gray. Third\, our robustness to visual defects and other difficult viewing conditions explains our ability to understand styles of pictorial textures\, like paint strokes.  Extensions of these basic ideas can explain depiction in many different visual styles\, including photographic tone reproduction\, line drawings\, silhouettes\, cartoons\, painterly styles\, and more. The proposed models of picture understanding could significantly inform future analysis of perceptual mechanisms\, picture aesthetics\, and the nature of different styles of depiction.\n\nZoom Link: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/carl-vondick-hosts-talk-with-aaron-hertzmann-adobe/
LOCATION:CSB 453\, Mudd Building\, 500 W 120th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251112T160000
DTEND;TZID=America/New_York:20251112T170000
DTSTAMP:20260403T154252
CREATED:20251112T151131Z
LAST-MODIFIED:20251112T151131Z
UID:2032-1762963200-1762966800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Next Meeting Info\n\n\nDate: Wednesday\, Nov 12\nTime: 4pm-5pm\nRoom: CEPSR 620\nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-3/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251107T160000
DTEND;TZID=America/New_York:20251107T170000
DTSTAMP:20260403T154252
CREATED:20251103T193100Z
LAST-MODIFIED:20251103T193100Z
UID:2024-1762531200-1762534800@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings about benchmarks and competition proposals. \nZoom Link: upon request @ ARNI@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-5/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251105T140000
DTEND;TZID=America/New_York:20251105T150000
DTSTAMP:20260403T154252
CREATED:20251022T211425Z
LAST-MODIFIED:20251028T143328Z
UID:2019-1762351200-1762354800@arni-institute.org
SUMMARY:Speaker: Bryan Li - ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Bio\nBryan Li is completing his PhD in NeuroAI at the University of Edinburgh\, under the supervision of Arno Onken and Nathalie Rochefort. His main PhD project focuses on building deep learning-based encoding models of the visual cortex that accurately predict neural activity in response to arbitrary visual stimuli. Recently\, he joined Dario Farina’s lab at Imperial College London as an Encode Fellow\, working on neuromotor interfacing and decoding.\n\nTitle (https://www.biorxiv.org/content/10.1101/2025.09.16.676524v2)\nMovie-trained transformer reveals novel response properties to dynamic stimuli in mouse visual cortex\n\nAbstract\nUnderstanding how the brain encodes complex\, dynamic visual stimuli remains a\nfundamental challenge in neuroscience. Here\, we introduce ViV1T\, a transformer-based model trained on natural movies to predict neuronal responses in mouse primary visual cortex (V1). ViV1T outperformed state-of-the-art models in predicting responses to both natural and artificial dynamic stimuli\, while requiring fewer parameters and reducing runtime. Despite being trained exclusively on natural movies\, ViV1T accurately captured core V1 properties\, including orientation and direction selectivity as well as contextual modulation\, despite lacking explicit feedback mechanisms. ViV1T also revealed novel functional features. The model predicted a wider range of contextual responses when using natural and model-generated surround stimuli compared to traditional gratings\, with novel model-generated dynamic stimuli eliciting maximal V1 responses. ViV1T also predicted that dynamic surrounds elicited stronger contextual modulation than static surrounds. Finally\, the model identified a subpopulation of neurons that exhibit contrast-dependent surround modulation\, switching their response to surround stimuli from inhibition to excitation when contrast decreases. These predictions were validated through semi-closed-loop in vivo recordings. Overall\, ViV1T establishes a powerful\, data-driven framework for understanding how brain sensory areas process dynamic visual information across space and time.\n\nZoom link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-frontier-models-for-neuroscience-and-behavior-working-group-2/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251105T113000
DTEND;TZID=America/New_York:20251105T123000
DTSTAMP:20260403T154252
CREATED:20251105T152843Z
LAST-MODIFIED:20251105T152843Z
UID:2028-1762342200-1762345800@arni-institute.org
SUMMARY:CTN: Yael Niv
DESCRIPTION:Seminar Time: 11:30am\nDate: Fri 11/7/25\nSeminar Location: JLG\, L5-084\nHost: Weijia Zhang\n\n\n\nTitle: Latent causes\, prediction errors\, and the organization of memory\n\nAbstract: No two events are alike. But still\, we learn\, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. We have suggested that this relies on parsing of incoming information into “clusters” according to inferred hidden (latent) causes. Moreover\, we have suggested that unexpected information (that is\, a prediction error) is key to this separation into clusters. In this talk\, I will demonstrate these ideas through behavioral experiments showing evidence for clustering and illustrate the effects of prediction errors on the organization of memory. I will then tie the different findings together into a hypothesis about how information about events is organized in our brain.
URL:https://arni-institute.org/event/ctn-yael-niv/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251029T150000
DTEND;TZID=America/New_York:20251029T163000
DTSTAMP:20260403T154252
CREATED:20251008T214532Z
LAST-MODIFIED:20251015T155911Z
UID:2010-1761750000-1761755400@arni-institute.org
SUMMARY:ARNI Distinguished Seminar Series: Leila Wehbe
DESCRIPTION:Bio: Leila Wehbe is an associate professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. Her work is at the interface of cognitive neuroscience and computer science. It combines naturalistic functional imaging with machine learning both to improve our understanding of the brain and to find insight to build better artificial systems. She is the recipient of an NSF CAREER award\, a Google faculty research award and an NIH CRCNS R01. Previously\, she was a postdoctoral researcher at UC Berkeley and obtained her PhD from Carnegie Mellon University \nTitle: Model prediction error reveals separate mechanisms for integrating multi-modal information in the human cortex \nAbstract: Language comprehension engages much of the human cortex\, extending beyond the canonical language system. Yet in everyday life\, language unfolds alongside other modalities\, such as vision\, that recruit these same distributed areas. Because language is often studied in isolation\, we still know little about how the brain coordinates and integrates multimodal representations. In this talk\, we use fMRI data from participants viewing 37 hours of TV series and movies to model the interaction of auditory and visual input. Using encoding models that predict brain activity from each stream\, we introduce a framework based on prediction error that reveals how individual brain regions combine multimodal information.
URL:https://arni-institute.org/event/arni-distinguished-seminar-series-leila-wehbe/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251027T150000
DTEND;TZID=America/New_York:20251027T160000
DTSTAMP:20260403T154252
CREATED:20251022T210922Z
LAST-MODIFIED:20251022T210922Z
UID:2018-1761577200-1761580800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Next Meeting Info\n\n\nDate: Monday\, October 27\nTime: 3-4pm\nRoom: CEPSR 620\n\nZoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-2/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T140000
DTEND;TZID=America/New_York:20251024T150000
DTSTAMP:20260403T154252
CREATED:20251022T132648Z
LAST-MODIFIED:20251022T132648Z
UID:2016-1761314400-1761318000@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings about benchmarks and competition proposals. \nZoom Link: upon request @ ARNI@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-4/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T113000
DTEND;TZID=America/New_York:20251024T130000
DTSTAMP:20260403T154252
CREATED:20250917T182936Z
LAST-MODIFIED:20251021T182719Z
UID:1992-1761305400-1761310800@arni-institute.org
SUMMARY:CTN: Anna Schapiro
DESCRIPTION:Title: Learning representations of specifics and generalities over time\n\nAbstract: There is a fundamental tension between storing discrete traces of individual experiences\, which allows recall of particular moments in our past without interference\, and extracting regularities across these experiences\, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems\, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days\, months\, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly\, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems\, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we rapidly learn novel information of specific and generalized types\, which we test across statistical learning\, inference\, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems\, with empirical and modeling investigations into how hippocampal replay shapes neocortical representations during sleep. Together\, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.\nZoom: Available upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/ctn-anna-schapiro/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251021T130000
DTEND;TZID=America/New_York:20251021T150000
DTSTAMP:20260403T154252
CREATED:20251008T213444Z
LAST-MODIFIED:20251008T221401Z
UID:2007-1761051600-1761058800@arni-institute.org
SUMMARY:Speaker: Jascha Achterberg ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Title:\nBuilding the brain’s efficient system-level architecture: optimisations across space\, time\, and multiple regions \nAbstract:\nThe computations a brain can perform are fundamentally constrained by physical realities: energetic resources are limited\, and time is precious. To understand why the brain works the way it does\, we must understand its function in the context of these constraints. Prior modeling work has successfully demonstrated how spatial energetic constraints drive structure-function co-optimization\, giving rise to many of the architectural features we observe across areas of neuroscience. By incorporating such physical constraints\, we can build complex systems-level models that are meaningfully constrained by physically measurable factors rather than arbitrary design choices. \nIn this talk\, I will expand on these spatial frameworks by introducing new work on temporal processing and signal precision constraints in neural networks. I will demonstrate how different optimization strategies within individual regions can be combined in heterogeneous multi-region models\, revealing how the brain trades off resource use across tasks and situations. Finally\, I will show how space and time interact in surprising ways to achieve efficient computation — principles that apply not only to the brain but to any large-scale distributed computing system. Together\, these advances bring us closer to understanding the general principles that enable sophisticated intelligence to emerge from physically and energetically constrained computing systems.
URL:https://arni-institute.org/event/speaker-jascha-achterberg-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251017T113000
DTEND;TZID=America/New_York:20251017T130000
DTSTAMP:20260403T154252
CREATED:20250923T155504Z
LAST-MODIFIED:20250923T155517Z
UID:2001-1760700600-1760706000@arni-institute.org
SUMMARY:CTN: Ilana Witten
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-illana-witten/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251010T113000
DTEND;TZID=America/New_York:20251010T130000
DTSTAMP:20260403T154252
CREATED:20250923T155316Z
LAST-MODIFIED:20251007T171403Z
UID:1999-1760095800-1760101200@arni-institute.org
SUMMARY:CTN: Maryam Shanechi
DESCRIPTION:Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology \nAbstract: A major challenge in neuroAI is to model\, decode\, and modulate the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. Toward addressing this challenge\, I will present our work on novel dynamical models of neural-behavioral data and applying them to enable a new generation of brain-computer interfaces for disorders such as major depression. First\, I will present a novel dynamical modeling framework that jointly describes neural-behavioral data\, dissociates behaviorally relevant neural dynamics\, and learns them more accurately. Then\, I will show how we can also predict the effect of inputs\, such as sensory stimuli or neurostimulation\, to dissociate intrinsic and input-driven neural dynamics. I further present how these models can incorporate multiple spatiotemporal scales of brain activity simultaneously\, from spikes to LFP to brain-wide neuroimaging. Finally\, I will discuss the challenge of developing AI algorithms for neurotechnology. I will present a framework that combines neural networks with stochastic state-space models to enable accurate yet flexible inference of brain states causally\, non-causally\, and even with missing neural samples. The above dynamical models can enable next-generation AI-driven neurotechnologies that restore lost motor and emotional function in diverse brain disorders such as paralysis and major depression.
URL:https://arni-institute.org/event/ctn-maryam-shanechi/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251008T140000
DTEND;TZID=America/New_York:20251008T150000
DTSTAMP:20260403T154252
CREATED:20250917T150021Z
LAST-MODIFIED:20251006T170343Z
UID:1991-1759932000-1759935600@arni-institute.org
SUMMARY:ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title:\nOmniMouse: Scaling properties of multi-modal\, multi-task Brain Models on 150B Neural Tokens \nAbstract:\nScaling data and artificial neural networks has transformed AI\, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions\, totaling more than 150 billion neural tokens recorded during natural movies\, images and parametric stimuli\, and behavior. We train multi-modal\, multi-task transformer models (1M–300M parameters) that support three regimes flexibly at test time: neural prediction (predicting neuronal responses from sensory input and behavior)\, behavioral decoding (predicting behavior from neural activity)\, neural forecasting (predicting future activity from current neural dynamics)\, or any combination of the three. We find that performance scales reliably with more data\, but gains from increasing model size saturate — suggesting that current brain models are limited by data rather than compute. This inverts the standard AI scaling story: in language and computer vision\, massive datasets make parameter scaling the primary driver of progress\, whereas in brain modeling — even in the mouse visual cortex\, a relatively simple and low-resolution system — models remain data-limited despite vast recordings. These findings highlight the need for richer stimuli\, tasks\, and larger-scale recordings to build brain foundation models. The observation of systematic scaling raises the possibility of phase transitions in neural modeling\, where larger and richer datasets might unlock qualitatively new capabilities\, paralleling the emergent properties seen in large language models. \nZoom: Upon request @ arni@columbia.edu \n 
URL:https://arni-institute.org/event/arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T140000
DTEND;TZID=America/New_York:20251003T150000
DTSTAMP:20260403T154252
CREATED:20250922T183104Z
LAST-MODIFIED:20250922T183104Z
UID:1994-1759500000-1759503600@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Biological Learning first fall 2025 working group session of the year! Pingsheng Li\, PhD student with Blake Richards at MILA\, will be presenting Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks.\n \nBrief discussion of how the group can all collaborate together on a project\, define a benchmark for ourselves with some metrics we care about\, and then the group will break into pods that each will develop methods towards solving the benchmark tasks. \nGoogle Meets: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-3/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T113000
DTEND;TZID=America/New_York:20251003T130000
DTSTAMP:20260403T154252
CREATED:20250923T155204Z
LAST-MODIFIED:20250923T155204Z
UID:1997-1759491000-1759496400@arni-institute.org
SUMMARY:CTN: Reza Shadmehr
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-reza-shadmehr/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250929T150000
DTEND;TZID=America/New_York:20250929T160000
DTSTAMP:20260403T154252
CREATED:20250917T145723Z
LAST-MODIFIED:20250922T184156Z
UID:1990-1759158000-1759161600@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Next Monday\, September 29\, we will continue our fall semester program with a group workshop on the topic of neural memory models.  First\, we will have a presentation from group member Max Bennett about our ongoing work on generalized neural memory systems that perform flexible updates based on learning instructions specified in natural language.  After the presentation\, we will spend some time in open discussion of memory models\, and hopefully discuss potential (interdisciplinary) projects for the group.\n\nNext Meeting Info\n\n\nDate: Monday\, September 29\nTime: 3-4pm\nRoom: CEPSR 620\nZoom: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-12/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250926T113000
DTEND;TZID=America/New_York:20250926T130000
DTSTAMP:20260403T154252
CREATED:20250902T200421Z
LAST-MODIFIED:20250923T155034Z
UID:1971-1758886200-1758891600@arni-institute.org
SUMMARY:CTN: Ann Kennedy
DESCRIPTION:Title: Neural computations underlying the regulation of motivated behavior \nAbstract: As we interact with the world around us\, we experience a constant stream of sensory inputs\, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state to produce behavior that is flexible and adaptive. In this talk\, I will present three recent stories from the lab exploring the dynamics and modulation of motivational states. First\, working with neural recordings from a hypothalamic nucleus involved in regulation of aggression\, I show how we relate the dynamical properties of neural populations to escalation of an aggressive motivational state. Next\, using methods from control theory and reinforcement learning\, I show that different sites of modulation within a neural circuit produce different resulting effects on behavior and neural activity. Finally\, I will show how theoretical models can reveal unexpected effects of neuromodulation on the dynamic regimes of recurrent neural networks\, illuminating the ways in which the brain might use small molecules to reshape its activity and thus modify behavior.
URL:https://arni-institute.org/event/ctn-ann-kennedy/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250922T150000
DTEND;TZID=America/New_York:20250922T160000
DTSTAMP:20260403T154252
CREATED:20250904T153706Z
LAST-MODIFIED:20250904T204820Z
UID:1977-1758553200-1758556800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Zoom Link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-10/
LOCATION:CSB 480\, Mudd Building\, 500 W 120th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250919T113000
DTEND;TZID=America/New_York:20250919T130000
DTSTAMP:20260403T154252
CREATED:20250902T200250Z
LAST-MODIFIED:20250917T183026Z
UID:1969-1758281400-1758286800@arni-institute.org
SUMMARY:CTN: Dani Bassett
DESCRIPTION:Title and Abstract: TBD \nZoom:\nMeeting ID: 993 3345 6502\nPasscode: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/ctn-dani-bassett/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
END:VCALENDAR