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ARNI Frontier Models for Neuroscience and Behavior Working Group (Priorly: Animal Behavior): Meeting 2
Speakers: Matt Whiteway and Mehdi Azabou Meeting Description: We will share progress on a benchmark using the IBL brain-wide map dataset Working 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…
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ARNI WG Multi-resource-cost optimization of neural network models: Mitya Chklovskii
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Can resource optimization explain neuronal morphology and placement? Abstract: TBD Zoom: https://columbiauniversity.zoom.us/j/98788275902?pwd=Lnw6VtoEMdGUg0YygbkJBF3uAKgjsO.1&jst=3
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CTN: Shaul Druckmann
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Neural dynamics of short term memory, from mice to human speech Abstract: 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…
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AI and Neuroscience/Cognitive Science Activities Brainstorming
Fairchild 700 1212 Amsterdam AveARNI 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…
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CTN: György Buzsáki
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesSeminar Time: 11:30am Date: Fri 6/6/25 Seminar Location: JLG, L5-084 Host: Erfan Zabeh Title: Selection and consolidation of memory
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Saturday Science
JLGSC 605 W 129th StreetIf 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…
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CTN: Preeya Khanna
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Mapping and Mending Dexterous Movement Control with Neurotechnology Abstract: 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…
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ARNI Continual Learning Working Group Project
CEPSR 620 Schapiro 530 W. 120th StTitle: Benchmark Development for Lifelong Learning in LLMs Abstract: 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. Zoom link: By request
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CTN: Andrew Saxe
Zoom Link: https://columbiauniversity.zoom.us/j/92032394293?pwd=ZkQBLK7LrSU7ku2zkvXTd2QEw4WUSn.1
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Speaker: Dr. Guillaume Lajoie – ARNI Frontier Models for Neuroscience and Behavior Working Group
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: POSSM: Generalizable, real-time neural decoding with hybrid state-space models Abstract: Real-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…
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CTN: Blake Richards
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Brain-like learning with exponentiated gradients Abstract: 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…
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Biological Learning Working Group Meeting
VirtualContinuation of the prior meeting. Zoom: Upon request @[email protected]
12 events found.
