CCN 2026 Satelite Event: Modeling and Understanding Human Brain Computation at Scale
2026-08-02 10:00 - 18:00 (America/New_York) | To Be Determined
Location: Zuckerman Institute (room TBD)
Date and Time: August 2nd, 10am to 6pm
About:
Recent advances in deep learning and improvements in the quantity and quality of available human brain-activity data (including functional MRI, EEG, MEG, and intracranial recordings) have made it possible to build accurate encoding models of the human brain that can predict neural activity for new visual and auditory stimuli in individual people, even with generalization to new individuals. In parallel, recent decoding models leverage prior information from generative multimodal models to extract rich perceptual and semantic content from brain activity with increasing fidelity. It remains unclear, however, how these technical advances can best be translated into theoretical advances (a better scientific understanding of human brain computation) and impactful applications for the benefit of humanity.
One important goal is to build human brain foundation models that are constrained simultaneously by rich stimulus data, large-scale diverse brain-activity data, and task performance requirements, so as to capture the computations performed by the human brain. This satellite event “Modeling and Understanding Human Brain Computation at Scale” brings together researchers who build neural network models that capture shared structure in neural responses across the human population at scale and use the models to drive theoretical progress on the computations underlying human cognition and perception. A central theme is methodology: What mapping functions, architectures, and training regimes achieve strong generalization and enable interpretation? The event aims to foster dialogue between those collecting and modeling large-scale human brain data and those asking what such models can tell us about how the brain works and how human brain foundation models might be applied for human benefit.

Speaker:Jarod Lévy and Lucy Zhang– ARNI Frontier Models for Neuroscience and Behavior Working Group
2026-09-07 15:00 - 16:00 | Virtual
Title: Brain2Qwerty: Noninvasive decoding of typed sentences from human brain activity
Abstract: Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brain-computer-interfaces, non-invasive alternatives are still lagging behind. Here, we present Brain2Qwerty v2, a model that can decode the production of natural sentences solely from real-time magnetoencephalography (MEG) recordings. By collecting 22,000 sentences typed by nine subjects, each recorded for 10 hours, our model leverages character, word and sentence-level representations to achieve an average word error rate (WER) of 39%. For our best participant, the model accurately decodes half of the sentences with one word error or less. Critically, decoding accuracy log-linearly improves with data volume, suggesting that the performance gap with intracranial approaches could be partially bridged through data scaling. We show that AI enables this performance in three main ways: the substitution of hand-crafted pipelines for event detection with deep learning, the finetuning of large language models to extract semantic representations, and the deployment of AI agents to iteratively refine our decoding pipeline via automated code development. Together, these results show that non-invasive brain-to-text decoding starts to operate at a level of accuracy previously thought exclusive to surgical implants, opening a path toward safe and efficient brain-computer-interfaces.
Zoom: Upon request @arni@columbia.edu

Speaker: Alexandre Pouget - ARNI Distinguish Seminar Series
2026-09-09 15:00 - 16:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Alexandre Pouget
Date and Time: May 21st at 3pm
Location: Zuckerman Institute Kavli Auditorium 9th Floor
Title: Neural Models of Compositionality
Abstract: Compositionality is widely regarded as one of the cornerstones of general intelligence. It refers to the ability to rapidly generate or learn new concepts by combining simpler ones according to an underlying syntax, as exemplified in natural language. Compositionality was long thought to be primarily a human capacity and widely considered incompatible with artificial neural networks. Recent neural models, however, have begun to challenge this view. I will present two such models: one focused on simple cognitive tasks, the other on the control of complex motor trajectories. In both cases, few-shot learning emerges through the discovery of compositional solutions. Remarkably, the latter approach captures key, and often counterintuitive, aspects of rodent behavior in escape tasks, precisely the kind of setting in which animals exhibit near zero-shot learning. I will also discuss how these findings connect naturally to more sophisticated forms of compositionality in humans, particularly the use of language to support zero-shot learning and inference.
Zoom link: Upon request @ arni@columbia.edu
