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X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250603T180000
DTEND;TZID=America/New_York:20250603T193000
DTSTAMP:20260424T052531
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:20250606T113000
DTEND;TZID=America/New_York:20250606T130000
DTSTAMP:20260424T052531
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:20250607T130000
DTEND;TZID=America/New_York:20250607T160000
DTSTAMP:20260424T052531
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:20250613T113000
DTEND;TZID=America/New_York:20250613T130000
DTSTAMP:20260424T052531
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250617T150000
DTEND;TZID=America/New_York:20250617T160000
DTSTAMP:20260424T052531
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:20250620T110000
DTEND;TZID=America/New_York:20250620T130000
DTSTAMP:20260424T052531
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:20250625T140000
DTEND;TZID=America/New_York:20250625T153000
DTSTAMP:20260424T052531
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:20250627T113000
DTEND;TZID=America/New_York:20250627T133000
DTSTAMP:20260424T052531
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
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