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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T140000
DTEND;TZID=America/New_York:20251003T150000
DTSTAMP:20260403T181717
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:20260403T181717
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:20260403T181717
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:20260403T181717
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:20260403T181717
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:20260403T181717
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T150000
DTEND;TZID=America/New_York:20250915T160000
DTSTAMP:20260403T181717
CREATED:20250910T204507Z
LAST-MODIFIED:20250910T204507Z
UID:1988-1757948400-1757952000@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Monday (9/15) the Continual Learning Group will have a presentation from group member Yunfan Zhang.  Yunfan will be sharing his ongoing work on developing a continual learning benchmark based on deriving up-to-date facts from news over time.\n\n\nDate: Monday\, September 15\nTime: 3-4pm\nRoom: CSB 480\nZoom: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-11/
LOCATION:CSB 480\, Mudd Building\, 500 W 120th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250912T113000
DTEND;TZID=America/New_York:20250912T130000
DTSTAMP:20260403T181717
CREATED:20250902T195737Z
LAST-MODIFIED:20250909T154600Z
UID:1963-1757676600-1757682000@arni-institute.org
SUMMARY:CTN: Naomi Leonard
DESCRIPTION:Title:\nFast and Flexible Group Decision-Making \nAbstract:\nA wide range of animals live and move in groups. Many animals do better in groups than alone when\, for example\, foraging for food\, migrating\, and avoiding predators. A key to group success is social interaction. Less well understood is how a group\, with no centralized control\, is capable of the fast and flexible decision-making required to carry out its tasks in an environment with uncertainty\, variability\, and rapid change. I will introduce an approach to modeling group decision-making dynamics that draws on biophysical models from computational neuroscience. Analysis of our model provides new insights into fast and flexible decision-making: how indecision can be broken as fast as it becomes costly\, how sensitivity to stimulus can be tuned as context and environment change\, how social heterogeneity can enhance stability and flexibility\, and how excitability (spiking) provides further agility and frugality. I will discuss the significance of these results for the study and design of collective intelligence in nature and technology.
URL:https://arni-institute.org/event/ctn-naomi-leonard/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250908T150000
DTEND;TZID=America/New_York:20250908T160000
DTSTAMP:20260403T181717
CREATED:20250904T153618Z
LAST-MODIFIED:20250904T204910Z
UID:1976-1757343600-1757347200@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-9/
LOCATION:CSB 480\, Mudd Building\, 500 W 120th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250905T113000
DTEND;TZID=America/New_York:20250905T130000
DTSTAMP:20260403T181717
CREATED:20250902T145542Z
LAST-MODIFIED:20250902T145542Z
UID:1960-1757071800-1757077200@arni-institute.org
SUMMARY:CTN: Christine Constantinople
DESCRIPTION:Title: Neural circuit mechanisms of value-based decision-making \nAbstract: \nThe value of the environment determines animals’ motivational states and sets expectations for error-based learning. But how are values computed? We developed a novel temporal wagering task with latent structure\, and used high-throughput behavioral training to obtain well-powered behavioral datasets from hundreds of rats that learned the structure of the task. We found that rats use distinct value computations for sequential decisions within single trials. Moreover\, these sequential decisions are supported by different brain regions\, suggesting that distinct neural circuits support specific types of value computations. I will discuss our ongoing efforts to delineate how distributed circuits in the orbitofrontal cortex and striatum coordinate complex value-based decisions.
URL:https://arni-institute.org/event/ctn-christine-constantinople/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250827T140000
DTEND;TZID=America/New_York:20250827T160000
DTSTAMP:20260403T181717
CREATED:20250826T144924Z
LAST-MODIFIED:20250826T144924Z
UID:1952-1756303200-1756310400@arni-institute.org
SUMMARY:Speakers: Vinam Arora and Ji Xia – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title and Abstracts:  \n1st Speaker: Vinam Arora\, UPenn\nTitle: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context\nAbstract: Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics\, yet remains a major challenge in large-scale in vivo recordings where anatomical and molecular labels are often unavailable. Here we introduce NuCLR\, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron’s role within the broader population. NuCLR employs a spatiotemporal transformer that captures both within-neuron dynamics and across-neuron interactions\, and is trained with a sample-wise contrastive objective that encourages stable\, discriminative embeddings across time. Across multiple open-access datasets\, NuCLR outperforms prior methods in both cell type and brain region classification. It enables zero-shot generalization to entirely new populations—without retraining or access to stimulus labels—offering a scalable approach for real-time\, functional decoding of neuron identity across diverse experimental settings. \n2nd Speaker: Ji Xia\, Columbia\nTitle: In painting the neural picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.\nAbstract: Understanding how the brain drives memory-guided movements requires recording neural activity from the motor cortex and interconnected subcortical areas. Neuropixels probes now allow simultaneous recordings from subsets of these areas\, but no single session captures all areas of interest\, and different neurons are sampled from each area across sessions. This poses a key challenge: how to integrate neural data across sessions to reconstruct the complete multi-area picture. We address this with a transformer-based autoencoder that aligns neural activity into a shared latent space across sessions and animals\, separately for each brain area\, including those not recorded in a given session. This approach enables single-trial analysis of multi-area neural dynamics from all areas of interest. I am now working on improving this method\, and will discuss both its present challenges and promising directions for future work. \nZoom: Upon request at @ arni@columbia.edu.
URL:https://arni-institute.org/event/speakers-vinam-arora-and-ji-xia-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:20250807T143000
DTEND;TZID=America/New_York:20250807T163000
DTSTAMP:20260403T181717
CREATED:20250703T154507Z
LAST-MODIFIED:20250729T153708Z
UID:1845-1754577000-1754584200@arni-institute.org
SUMMARY:Speaker: Kwabena Boahen ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Title: From 2D Chips to 3D Brains \nAbstract: \nArtificial intelligence (AI) realizes a synaptocentric conception of the learning brain with dot-products and advances by performing twice as many multiplications every two months. But the semiconductor industry tiles twice as many multipliers on a chip only every two years. Moreover\, the returns from tiling these multipliers ever more densely now diminish\, because signals must travel relatively farther and farther\, expending energy and exhausting heat that scales quadratically. As a result\, communication is now much more expensive than computation. Much more so than in biological brains\, where energy-use scales linearly rather than quadratically with neuron count. That allows an 86-billion-neuron human brain to use as little power as a single lightbulb (25W) rather than as much as the entire US (3TW). Hence\, rescaling a chip’s energy-use from quadratic to linear is critical to scale AI sustainably from trillion (1012) parameters (mouse scale) today to a quadrillion (1015) parameters (human scale) in the next five years. But this would require communication cost to be reduced radically. Towards that end\, I will present a recent re-conception of the brain’s fundamental unit of computation that sparsifies signals by moving away from synaptocentric learning with dot-products to dendrocentric learning with sequence detectors. \nZoom: Request @ ARNI@columbia.edu
URL:https://arni-institute.org/event/speaker-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250730T140000
DTEND;TZID=America/New_York:20250730T160000
DTSTAMP:20260403T181717
CREATED:20250703T150730Z
LAST-MODIFIED:20250723T174753Z
UID:1842-1753884000-1753891200@arni-institute.org
SUMMARY:Speaker: Memming Park  – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title: Meta-dynamical state space modeling for integrative neural data analysis \nAbstract:\nUncovering the organizing principles of neural systems requires integrating information across diverse datasets—each alone offering a limited view and signal-to-noise ratio\, but together revealing coherent dynamical structures. We present a meta-dynamical state-space modeling framework that learns a shared solution space of neural dynamics from heterogeneous recordings across sessions\, animals\, and tasks. By capturing cross-dataset similarity and variability on a low-dimensional manifold that spans a space of dynamical systems\, our approach enables few-shot inference\, rapid adaptation to new recordings\, and discovery of latent dynamical motifs that underlie behavior. We demonstrate its utility in modeling motor cortex activity\, revealing dynamics that generalize across individuals and track the change in dynamics during learning. We argue that for understanding neural computation and real-time neuroscience applications\, our approach is well-suited as a foundation model for integrative neuroscience. \nZoom: Request @arni@columbia.edu
URL:https://arni-institute.org/event/speaker-memming-park-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:20250715T180000
DTEND;TZID=America/New_York:20250715T193000
DTSTAMP:20260403T181717
CREATED:20250709T154233Z
LAST-MODIFIED:20250709T154233Z
UID:1871-1752602400-1752607800@arni-institute.org
SUMMARY:AI and Neuroscience/Cognitive Science Activities Brainstorming
DESCRIPTION:ARNI will host an informal brainstorming session on July 15th (ZI Education Lab) focused on developing AI and neuroscience/cognitive science activities for K–12 students. The goal is to create engaging ways to help young learners better understand the brain and artificial intelligence. Trainees are encouraged to attend—if you’re interested in making an impact on youth education\, this is a great opportunity to get involved. Join if you are free! There will be free pizza! \nThis is the registration form!
URL:https://arni-institute.org/event/ai-and-neuroscience-cognitive-science-activities-brainstorming-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250709T110000
DTEND;TZID=America/New_York:20250709T123000
DTSTAMP:20260403T181717
CREATED:20250709T153527Z
LAST-MODIFIED:20250709T153527Z
UID:1868-1752058800-1752064200@arni-institute.org
SUMMARY:Biological Learning Working Group Meeting
DESCRIPTION:Continuation of the prior meeting. \nZoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/biological-learning-working-group-meeting/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250627T113000
DTEND;TZID=America/New_York:20250627T133000
DTSTAMP:20260403T181717
CREATED:20250407T145614Z
LAST-MODIFIED:20250611T182613Z
UID:1629-1751023800-1751031000@arni-institute.org
SUMMARY:CTN: Blake Richards
DESCRIPTION:Title: Brain-like learning with exponentiated gradients \nAbstract: Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However\, it produces ANNs that are a poor phenomenological fit to biology\, making them less relevant as models of the brain. Specifically\, it violates Dale’s law\, by allowing synapses to change from excitatory to inhibitory\, and leads to synaptic weights that are not log-normally distributed\, contradicting experimental data. Here\, starting from first principles of optimisation theory\, we present an alternative learning algorithm\, exponentiated gradient (EG)\, that respects Dale’s Law and produces log-normal weights\, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD\, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether\, our results show that EG is a superior learning algorithm for modelling the brain with ANNs. \nZoom Link: By Request
URL:https://arni-institute.org/event/ctn-blake-richards/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250625T140000
DTEND;TZID=America/New_York:20250625T153000
DTSTAMP:20260403T181717
CREATED:20250604T173448Z
LAST-MODIFIED:20250703T150230Z
UID:1787-1750860000-1750865400@arni-institute.org
SUMMARY:Speaker: Dr. Guillaume Lajoie - ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title: POSSM: Generalizable\, real-time neural decoding with hybrid state-space models \nAbstract: \nReal-time decoding of neural spiking data is a core aspect of neurotechnology applications such as brain-computer interfaces\, where models are subject to strict latency constraints. Traditional methods\, including simple recurrent neural networks\, are fast and lightweight but are less equipped for generalization to unseen data. In contrast\, recent Transformer-based approaches leverage large-scale neural datasets to attain strong generalization performance. However\, these models typically have much larger computational requirements and are not suitable for settings requiring low latency or limited memory. To address these shortcomings\, we present POSSM\, a novel architecture that combines individual spike tokenization and an input cross-attention module with a recurrent state-space model (SSM) backbone\, thereby enabling (1) fast and causal online prediction on neural activity and (2) efficient generalization to new sessions\, individuals\, and tasks through multi-dataset pre-training. We evaluate our model’s performance in terms of decoding accuracy and inference speed on monkey reaching datasets\, and show that it extends to clinical applications\, namely handwriting and speech decoding. Notably\, we demonstrate that pre-training on monkey motor-cortical recordings improves decoding performance on the human handwriting task\, highlighting the exciting potential for cross-species transfer. In all of these tasks\, we find that POSSM achieves comparable decoding accuracy with state-of-the-art Transformers\, at a fraction of the inference cost. These results suggest that hybrid SSMs may be the key to bridging the gap between accuracy\, inference speed\, and generalization when training neural decoders for real-time\, closed-loop applications. \nZoom Link: Request via email arni@columbia.edu
URL:https://arni-institute.org/event/speaker-dr-guillaume-lajoie-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
CATEGORIES:ARNI Frontier Models for Neuroscience and Behavior Working Group
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250620T110000
DTEND;TZID=America/New_York:20250620T130000
DTSTAMP:20260403T181717
CREATED:20250407T145439Z
LAST-MODIFIED:20250421T152336Z
UID:1627-1750417200-1750424400@arni-institute.org
SUMMARY:CTN: Andrew Saxe
DESCRIPTION:Zoom Link: https://columbiauniversity.zoom.us/j/92032394293?pwd=ZkQBLK7LrSU7ku2zkvXTd2QEw4WUSn.1
URL:https://arni-institute.org/event/cnt-andrew-saxe/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250617T150000
DTEND;TZID=America/New_York:20250617T160000
DTSTAMP:20260403T181717
CREATED:20250613T133359Z
LAST-MODIFIED:20250613T140857Z
UID:1804-1750172400-1750176000@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Project
DESCRIPTION:Title: Benchmark Development for Lifelong Learning in LLMs\n\nAbstract: The ARNI Continual Learning working group continues its work towards developing a benchmark for lifelong learning in LLMs.  Discussions will be centered around learning over time as well as catastrophic forgetting in LLM post-training.\nZoom link: By request
URL:https://arni-institute.org/event/arni-continual-learning-working-group-project-8/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250613T113000
DTEND;TZID=America/New_York:20250613T130000
DTSTAMP:20260403T181717
CREATED:20250610T145532Z
LAST-MODIFIED:20250610T145535Z
UID:1794-1749814200-1749819600@arni-institute.org
SUMMARY:CTN: Preeya Khanna
DESCRIPTION:Title: Mapping and Mending Dexterous Movement Control with Neurotechnology\n \nAbstract: Dexterous movement is a hallmark of human motor ability\, enabling us to interact skillfully with our environment. The loss of this capability due to movement disorders\, such as Parkinson’s disease or stroke\, strips individuals of independence and quality of life. This talk explores the neural underpinnings of dexterity\, focusing on how the nervous system integrates sensory and motor signals to achieve precise control. We then examine how these mechanisms break down in movement disorders\, leading to impaired motor function. Finally\, we turn to neuroengineering technologies which aim to restore movement in affected individuals. By leveraging advances in neural interfaces and wearable systems\, we are seeking to design systems to repair motor function. Overall\, we highlight our highly interdependent scientific and translational goals to understand and restore complex movement. 
URL:https://arni-institute.org/event/ctn-preeya-khanna/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250607T130000
DTEND;TZID=America/New_York:20250607T160000
DTSTAMP:20260403T181717
CREATED:20250603T204332Z
LAST-MODIFIED:20250603T204332Z
UID:1782-1749301200-1749312000@arni-institute.org
SUMMARY:Saturday Science
DESCRIPTION:If you have children or know any young people interested in hands-on STEM fun\, bring them to Saturday Science\, hosted by Columbia’s CUNO group! The event takes place on Saturday\, June 7th at the Jerome L. Greene Science Center (605 West 129th Street). Kids will enjoy engaging\, interactive activities while exploring exciting science concepts. ARNI will also host a station featuring an image classifier activity that highlights the similarities between how AI and the brain process information. You can attend at any time but we also suggest that you complete this registration form! 
URL:https://arni-institute.org/event/saturday-science/
LOCATION:JLGSC\, 605 W 129th Street
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250606T113000
DTEND;TZID=America/New_York:20250606T130000
DTSTAMP:20260403T181717
CREATED:20250603T203005Z
LAST-MODIFIED:20250603T203005Z
UID:1774-1749209400-1749214800@arni-institute.org
SUMMARY:CTN: György Buzsáki
DESCRIPTION:Seminar Time: 11:30am\nDate: Fri 6/6/25\nSeminar Location: JLG\, L5-084\nHost: Erfan Zabeh\n\n \nTitle: Selection and consolidation of memory
URL:https://arni-institute.org/event/ctn-gyorgy-buzsaki/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250603T180000
DTEND;TZID=America/New_York:20250603T193000
DTSTAMP:20260403T181717
CREATED:20250603T203350Z
LAST-MODIFIED:20250603T203350Z
UID:1778-1748973600-1748979000@arni-institute.org
SUMMARY:AI and Neuroscience/Cognitive Science Activities Brainstorming
DESCRIPTION:ARNI will host an informal brainstorming session on June 3rd at 6pm (Fairchild 700) focused on developing AI and neuroscience/cognitive science activities for K–12 students. The goal is to create engaging ways to help young learners better understand the brain and artificial intelligence. Trainees are encouraged to attend—if you’re interested in making an impact on youth education\, this is a great opportunity to get involved. Join if you are free! \nThis is the registration form!
URL:https://arni-institute.org/event/ai-and-neuroscience-cognitive-science-activities-brainstorming/
LOCATION:Fairchild 700\, 1212 Amsterdam Ave
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250530T113000
DTEND;TZID=America/New_York:20250530T130000
DTSTAMP:20260403T181717
CREATED:20250528T130320Z
LAST-MODIFIED:20250528T130320Z
UID:1769-1748604600-1748610000@arni-institute.org
SUMMARY:CTN: Shaul Druckmann
DESCRIPTION:Title: Neural dynamics of short term memory\, from mice to human speech \nAbstract: Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits\, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed\, even in extremely simplified experimental conditions\, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning\, i.e.\, a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. \nI will first briefly discuss our work on motor preparatory activity in a delayed response task in mice were we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. We first revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity\, beyond what was thought to be the case experimentally and theoretically. Second\, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information\, as if the circuit was setup to make informative dimensions stiff\, i.e.\, resistive to perturbations\, leaving uninformative dimensions sloppy\, i.e.\, sensitive to perturbations. \nI will then present unpublished work on understanding the neural dynamics underlying preparation of speech. First\, we found that as words or brief sentences are being prepared to be spoken\, we can decode not just the first phoneme to be spoken\, but rather multiple components of the speech sequence are decodable\, i.e.\, prepared in parallel. Second\, we found that unlike some previous descriptions of sequence preparation\, components were not always separated into distinct subspaces\, but rather were found in overlapping subspaces with a structured organization of their neural geometry by element and sequence position.
URL:https://arni-institute.org/event/ctn-shaul-druckmann/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250529T150000
DTEND;TZID=America/New_York:20250529T170000
DTSTAMP:20260403T181717
CREATED:20250415T195746Z
LAST-MODIFIED:20250528T134249Z
UID:1649-1748530800-1748538000@arni-institute.org
SUMMARY:ARNI WG Multi-resource-cost optimization of neural network models: Mitya Chklovskii
DESCRIPTION:Title: Can resource optimization explain neuronal morphology and placement? \nAbstract: TBD \nZoom: https://columbiauniversity.zoom.us/j/98788275902?pwd=Lnw6VtoEMdGUg0YygbkJBF3uAKgjsO.1&jst=3
URL:https://arni-institute.org/event/arni-wg-multi-resource-cost-optimization-of-neural-network-models-mitya-chklovskii/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250528T140000
DTEND;TZID=America/New_York:20250528T140000
DTSTAMP:20260403T181717
CREATED:20250507T155005Z
LAST-MODIFIED:20250522T203309Z
UID:1694-1748440800-1748440800@arni-institute.org
SUMMARY:ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior): Meeting 2
DESCRIPTION:Speakers: Matt Whiteway and Mehdi Azabou \nMeeting Description: We will share progress on a benchmark using the IBL brain-wide map dataset \nWorking group 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. \nLocation: ZI L4-082 \nZoom Link: https://columbiauniversity.zoom.us/j/95445157877?pwd=FawUA2r6I1bxMN3W2uyok4YtF6QdpZ.1
URL:https://arni-institute.org/event/arni-frontier-models-for-neuroscience-and-behavior-working-group-priorly-animal-behavior-meeting-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250523T113000
DTEND;TZID=America/New_York:20250523T130000
DTSTAMP:20260403T181717
CREATED:20250520T163121Z
LAST-MODIFIED:20250520T173222Z
UID:1738-1747999800-1748005200@arni-institute.org
SUMMARY:CTN: Erin Rich
DESCRIPTION:Title: Dynamics of evaluation and choice in the orbitofrontal cortex\n \nAbstract: The orbitofrontal cortex is known to be important for evaluation\, choice\, and motivated behavior\, but theories on its precise role have continually evolved. In this talk\, I will discuss new perspectives on evaluation and choice in the orbitofrontal cortex in light of work from our lab and others that emphasizes information encoded by neural populations and their dynamics. I will argue that a useful framework to understand these data comes from theories of embodied decision-making\, in which moment-by-moment processing is determined by ongoing interactions between the individual and their environment.
URL:https://arni-institute.org/event/ctn-erin-rich/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250521T130000
DTEND;TZID=America/New_York:20250521T140000
DTSTAMP:20260403T181717
CREATED:20250324T152854Z
LAST-MODIFIED:20250324T152854Z
UID:1598-1747832400-1747836000@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-7/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250516T113000
DTEND;TZID=America/New_York:20250516T130000
DTSTAMP:20260403T181717
CREATED:20250513T151616Z
LAST-MODIFIED:20250513T151616Z
UID:1718-1747395000-1747400400@arni-institute.org
SUMMARY:CTN: Gaia Tavoni
DESCRIPTION:Title: A Unified Framework for Sensory Coding in Feedback-Modulated Canonical Networks \n\nAbstract: In recent decades\, the principles of neural coding have largely been studied at the level of single neurons or unimodal sensory networks. However\, brain networks interact in complex ways\, often integrating information across sensory modalities. Notably\, we lack a theoretical framework for understanding coding in interacting networks\, where information can converge from different sources. In this talk\, I will introduce a fully analytical normative framework for neural coding in feedback-modulated canonical networks\, a ubiquitous motif in the brain. In our model\, feedback is exogenous rather than endogenous to a given modality\, mediating interactions between the senses. Our theory demonstrates that predictive coding is an emergent property of efficient codes\, unifying two primary coding schemes. It further demonstrates how the computational principles of efficient and predictive coding can be implemented at the algorithmic level by a shared neural substrate\, with different network components performing distinct and interpretable mathematical operations. Finally\, our theory explains a variety of observed unimodal and multimodal sensory effects within the same normative framework and makes new predictions about the role of feedback in optimizing multimodal codes. I will conclude by showing how optimal sensory codes can be learned in biological networks through distributed Hebbian learning. Altogether\, our theory provides a unifying view of computational\, algorithmic\, and implementational principles of sensory coding in feedback-modulated canonical networks.
URL:https://arni-institute.org/event/ctn-gaia-tavoni/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250514T130000
DTEND;TZID=America/New_York:20250514T140000
DTSTAMP:20260403T181717
CREATED:20250324T152812Z
LAST-MODIFIED:20250324T152812Z
UID:1596-1747227600-1747231200@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-6/
END:VEVENT
END:VCALENDAR