Events
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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]
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AI and Neuroscience/Cognitive Science Activities Brainstorming
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…
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Speaker: Memming Park – ARNI Frontier Models for Neuroscience and Behavior Working Group
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: Meta-dynamical state space modeling for integrative neural data analysis Abstract: Uncovering the organizing principles of neural systems requires integrating information across diverse datasets—each alone offering a limited view and…
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Speaker: Kwabena Boahen ARNI WG Multi-resource-cost optimization of neural network models
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: From 2D Chips to 3D Brains Abstract: Artificial intelligence (AI) realizes a synaptocentric conception of the learning brain with dot-products and advances by performing twice as many multiplications every…
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Speakers: Vinam Arora and Ji Xia – ARNI Frontier Models for Neuroscience and Behavior Working Group
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle and Abstracts: 1st Speaker: Vinam Arora, UPenn Title: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context Abstract: Identifying the functional identity of individual neurons is essential…
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CTN: Christine Constantinople
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Neural circuit mechanisms of value-based decision-making Abstract: The value of the environment determines animals’ motivational states and sets expectations for error-based learning. But how are values computed? We developed…
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ARNI Continual Learning Working Group Project
CSB 480 Mudd Building, 500 W 120th StreetZoom link: Upon request @[email protected]
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CTN: Naomi Leonard
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Fast and Flexible Group Decision-Making Abstract: A wide range of animals live and move in groups. Many animals do better in groups than alone when, for example, foraging for…
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ARNI Continual Learning Working Group Project
CSB 480 Mudd Building, 500 W 120th StreetMonday (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…
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CTN: Dani Bassett
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle and Abstract: TBD Zoom: Meeting ID: 993 3345 6502 Passcode: Upon request @ [email protected]