BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARNI - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ARNI
X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250730T140000
DTEND;TZID=America/New_York:20250730T160000
DTSTAMP:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250509T113000
DTEND;TZID=America/New_York:20250509T130000
DTSTAMP:20260403T194801
CREATED:20250506T174222Z
LAST-MODIFIED:20250506T174312Z
UID:1691-1746790200-1746795600@arni-institute.org
SUMMARY:CTN: Stephanie Palmer
DESCRIPTION:Title:\nHow behavioral and evolutionary constraints sculpt early visual processing\n \nAbstract:\nBiological systems must selectively encode partial information about the environment\, as dictated by the capacity constraints at work in all living organisms. For example\, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays\, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints\, but it treats all input features equally. Not all inputs are\, however\, of equal value to the organism. Our work quantifies whether and how the brain selectively encodes stimulus features\, specifically predictive features\, that are most useful for fast and effective movements. We have shown that efficient predictive computation starts at the earliest stages of the visual system\, in the retina. We borrow techniques from statistical physics and information theory to assess how we get terrific\, predictive vision from these imperfect (lagged and noisy) component parts. In broader terms\, we aim to build a more complete theory of efficient encoding in the brain\, and along the way have found some intriguing connections between formal notions of coarse graining in biology and physics.
URL:https://arni-institute.org/event/ctn-stephanie-palmer/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250508T140000
DTEND;TZID=America/New_York:20250508T150000
DTSTAMP:20260403T194801
CREATED:20250501T211001Z
LAST-MODIFIED:20250505T155052Z
UID:1686-1746712800-1746716400@arni-institute.org
SUMMARY:ARNI Emerging Researchers Talk Series #3: Matteo Alleman
DESCRIPTION:Title: Discovery of categorical concepts\n\nAbstract: We seem to like reasoning in terms of discrete\, logical categories\, even in the face of continuous variation. A generous interpretation of this phenomenon is that we create abstractions about the world which enable powerful generalization to new situations — once I know what “hot-and-sour” is\, I can instantly know if I will like any dish. But state of the art learning systems\, and as far as we can tell our own brains\, use highly distributed\, continuous representations. A way that we reconcile these two ideas — abstract symbols and continuous representations — is to imagine that there are certain “component-level” directions in the vector space which are re-used across all instances which contain the component. Like the famous “king:queen :: man:woman” square from Miklov et al (2013). This provides a clear way of encoding abstract categories in continuous vector space\, but is it possible to go the other way\, and infer the categories from the vectors?\nThe first thing that may come to mind when hearing about “inferring categories” is clustering. But this has a limitation — you cannot put together “king” and “man” into a “male” category while also clustering “king” and “queen” into a “monarch” one. It also doesn’t quite seem right to say that “king” is 0.5 “male” and 0.5 “monarch” (a soft clustering)\, or that one cluster is a subset of the other (a hierarchical clustering). Instead\, I want a way to group together items into multiple clusters based on their vector representations. When data are free to belong to multiple clusters (not in a sum-to-one probabilistic way) it becomes most natural to think of the problem as a matrix factorization\, where we are trying to express our data as the product of an otherwise unconstrained binary matrix of item-cluster assignments (call it S)\, and a real-valued matrix of cluster means (call it W)\, i.e. X = SW. That is the model I will present on.\nZoom Link: https://columbiauniversity.zoom.us/j/92200158294?pwd=nGTC4FSPz2adgaOrOC0kNfY5Nr1vYq.1&jst=3
URL:https://arni-institute.org/event/arni-emerging-researchers-talk-series-3-matteo-alleman/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250507T130000
DTEND;TZID=America/New_York:20250507T140000
DTSTAMP:20260403T194801
CREATED:20250324T152647Z
LAST-MODIFIED:20250324T152647Z
UID:1594-1746622800-1746626400@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-5/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250505T083000
DTEND;TZID=America/New_York:20250506T170000
DTSTAMP:20260403T194801
CREATED:20250414T161133Z
LAST-MODIFIED:20250414T161133Z
UID:1640-1746433800-1746550800@arni-institute.org
SUMMARY:Workshop on Emerging Trends in AI
DESCRIPTION:May 5th and 6th\n8:30am to 5:30pm \nPulitzer Hall – 2950 Broadway New York\, NY 10027 \nThis two-day workshop brings together leading experts in machine learning (ML) and neuroscience to examine two emerging themes: (1) the relationship between brain resilience and algorithmic robustness\, and (2) the role of ML-driven data generation in social sciences and the possible acceleration of scientific discovery. The workshop will hold two panels: one on how insights from ML and neuroscience can potentially inform each other toward the development of more resilient and robust systems\, and the second on the ethical and practical implications of synthetic data in shaping research outcomes and policy decisions. \nThe workshop will be hosted by Columbia Engineering\, the NSF AI Institute for Artificial and Natural Intelligence (ARNI)\, and Simons Institute for the Theory of Computing. \nRegister here!
URL:https://arni-institute.org/event/workshop-on-emerging-trends-in-ai/
LOCATION:Pulitzer Hall\, 2950 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250502T113000
DTEND;TZID=America/New_York:20250502T130000
DTSTAMP:20260403T194801
CREATED:20250429T152926Z
LAST-MODIFIED:20250429T152926Z
UID:1682-1746185400-1746190800@arni-institute.org
SUMMARY:CTN: Kenneth Harris
DESCRIPTION:Title: Multidimensional structure of activity in transcriptomically identified cortical cell types \nAbstract: The cerebral cortex is comprised of hundreds of distinct cell types\, connected into a network that underpins cognition.  To characterize the geometry of population activity in these cells\, we recorded from thousands of neurons simultaneously in mouse visual cortex\, and used post-hoc in situ transcriptomics to characterize their fine subtypes. We found that cortical population activity is organized around a single dimension shared between the subspace of spontaneous activity and responses to natural image stimuli\, and that a neuron’s coupling with this dimension can be predicted from a its transcriptome. \nSpontaneous population activity differed substantially with the animal’s behavioral state.  Population activity in alert states could be classified by two primary dimensions\, the first distinguishing locomoting vs. stationary periods\, and the second correlating with running speed; neurons correlated positively and negatively with both dimensions.  In non-alert states\, spontaneous activity was organized around a single dominant dimension which spontaneously oscillated\, and was close to orthogonal to the two running-related dimensions.  The coupling of neurons with this shared dimension was almost always non-negative. \nNatural image stimuli drove population activity in a subspace that overlapped the subspace of spontaneous activity only in one dimension: the dimension that was active in spontaneous oscillations of non-alert states. Even though this dimension was spontaneously oscillatory only during non-alert states\, sensory responses along this dimension were larger during running periods. This single shared dimension accounted for a larger fraction of total stimulus-related variance in inhibitory than excitatory neurons\, and in superficial than deep excitatory populations. \nThe neurons to these three dimensions could be predicted from their transcriptomes.  This prediction was weaker amongst superficial excitatory neurons\, whose strongest transcriptomic prediction was found for coupling to the oscillatory dimension. \nWe conclude that cortical activity is organized around a single dimension shared between spontaneous oscillations and natural image responses\, and hypothesize that this dimension encodes the salience of sensory or non-sensory messages broadcast from visual cortex to other cortical regions.
URL:https://arni-institute.org/event/ctn-kenneth-harris/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250430T140000
DTEND;TZID=America/New_York:20250430T160000
DTSTAMP:20260403T194801
CREATED:20250128T200004Z
LAST-MODIFIED:20250414T163102Z
UID:1479-1746021600-1746028800@arni-institute.org
SUMMARY:ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior)
DESCRIPTION:Description: Advances in neurotechnology and behavioral tracking have enabled the collection of large-scale neural and behavioral datasets\, offering new opportunities to study brain function in complex settings. However\, researchers face significant challenges in integrating and analyzing data across different brain regions\, individuals\, and behavioral contexts. Inspired by recent successes in large-scale “foundation” models in natural language and computational biology\, there is growing interest in developing both neurofoundation models—which learn from diverse neural recordings—and behavior foundation models—which capture structure in high-dimensional behavioral data. These models can be used for downstream tasks such as neural encoding and decoding\, cell-type classification\, and behavioral prediction. The goal of this working group is to explore this emerging field\, address challenges in scaling these models\, identify needs for benchmarks and tools to accelerate progress\, and determine how these models can be used to answer scientific questions in both neuroscience and behavioral research. \nMeeting Description: Our inaugural meeting will begin with a short talk summarizing the landscape of foundation models for neuroscience and behavior\, highlighting recent advances\, key challenges\, and opportunities for improvement. This will be followed by an open discussion to define the group’s collaborative focus and shared priorities. \nZoom Link: https://columbiauniversity.zoom.us/j/95445157877?pwd=FawUA2r6I1bxMN3W2uyok4YtF6QdpZ.1
URL:https://arni-institute.org/event/arni-animal-behavior-working-group-meeting/
LOCATION:Zuckerman Institute – L7-119\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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:20260403T194801
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
END:VCALENDAR