Speaker: Mia Dai – ARNI Frontier Models for Neuroscience and Behavior Working Group
2026-06-08 15:00 - 16:00 (America/New_York) | Virtual
Speakers: Mia Dai, PhD student in the Paninski Group at Columbia University
Title: Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding
Abstract: A deeper understanding of brain function requires a precise, structured characterization of behavior. Yet, capturing behavior from video in a form suitable for scientific analysis remains a fundamental challenge. Many prior studies represent behavior via pose estimation or nonlinear video embeddings. However, pose tracking discards rich information beyond predefined keypoints, while nonlinear video embeddings lack interpretability. We address this limitation with SABLE (Sparse-view Animal Behavior Latent Embeddings), a self-supervised framework that leverages geometric inductive biases to learn behavior representations. By augmenting a multi-view transformer with priors from monocular depth and pose estimation, SABLE reconstructs 3D animal behavior from extremely sparse views while learning explicit 3D latent structure. Without ground-truth 3D labels, it reliably recovers 3D behavior from two-view videos, whereas state-of-the-art methods fail or yield degenerate solutions. Through comprehensive evaluation, we demonstrate that SABLE learns 3D representations that match or exceed prior SOTA performance in neural encoding and decoding. Our method establishes 3D-aware video embeddings that capture complex behavior, opening new avenues for studying brain-behavior relationships.
Zoom: Upon request @ arni@columbia.edu

CTN: Bence Olveczky (Harvard University) - June 26, 2026
2026-06-26 11:30 - 13:00 | Zuckerman Institute- Kavli Auditorium 9th Fl, 3227 Broadway, NY
Bence Olveczky
Location: ZI
Date: June 26, 2026
Time: 11:30am
Zoom: Upon Request @ arni@Columbia.edu
Title: Neural circuits underlying learned motor sequences.
Abstract: Our ability to sequence movements and actions in response to unpredictable environmental events underlies our rich and adaptive behavioral repertoire. Such flexible behaviors contrast with overtrained, or automatic, motor sequences directed at specific tasks and executed the same way every time. We probed how neural circuits underlie these distinct forms of motor sequence execution by training rats on a ‘piano task’, in which the same motor sequence can be generated in response to unpredictable cues or overtrained to the point of automaticity. By measuring and manipulating neural activity in motor cortex and sensorimotor striatum, we delineate the logic by which these circuits combine to generate both flexible and automatic motor sequences.

Speaker: Konstantinos Barmpas (Imperial College London) and Jarod Lévy (Meta FAIR Paris and Inria MIND) – ARNI Frontier Models for Neuroscience and Behavior Working Group
2026-06-29 15:00 - 16:00 | Virtual
Konstantinos Barmpas (Postdoc Imperial College London)
Title: NeuroRVQ – Multi-Scale Biosignal Tokenization for Generative Foundation Models
Abstract: Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, yielding representations that are rich but challenging for machine learning. Foundation models trained to predict masked signal tokens have shown promise in learning generalizable biosignal representations, yet their performance depends on the tokenizer’s ability to preserve high-frequency dynamics and reconstruct signals with high fidelity. We introduce NeuroRVQ, a modality-adaptive biosignal tokenizer family designed for high-fidelity signal reconstruction. To capture the full frequency spectrum, NeuroRVQ decomposes biosignals into frequency-specific representations via multi-scale temporal convolutions, each encoded into hierarchical RVQ codebooks to preserve high-frequency detail, combined with a novel phase-aware training loss that respects the circular topology of Fourier phase. By tuning the temporal resolution, number and size of temporal kernels and RVQ depth, this design adapts to the spectro-temporal characteristics of each biosignal modality. To validate that tokenizer quality drives downstream performance, we train a simple masked-token foundation model for each modality (NeuroRVQ-FM) using the corresponding NeuroRVQ tokenizer. The NeuroRVQ-FM family achieves competitive or superior downstream performance compared to existing modality-specific foundation models, demonstrating that high-fidelity tokenization is a critical factor for effective biosignal modeling.

Jarod Lévy (PhD Student Meta FAIR/Inria)
Title: DANCE – Detect and Classify Events in EEG
Abstract: Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to known event onsets. However, while available in controlled experiments, such onsets are absent in continuous real-world monitoring. Here, we introduce DANCE, a deep learning pipeline that frames neural decoding as a set-prediction problem and jointly detects and classifies events directly from raw, unaligned signals. Evaluated separately on ten datasets curated from the literature with a wide variety of event types (ranging from milliseconds to minutes in duration), our model outperforms existing methods on a broad range of cognitive, clinical and BCI tasks. This single architecture establishes a new state of the art in the competitive task of seizure monitoring and matches the accuracy of onset-informed models for BCI tasks. Overall, our method marks a step towards end-to-end asynchronous neural decoding models.

Zoom: upon request @ arni@columbia.edu

ARNI Continual Learning Working Group
2026-06-30 15:00 - 16:00 | CSB 453, Mudd Building, 500 W 120th Street
Continuation from prior meetings.
Zoom: Upon request @ arni@columbia.edu

CCN 2026 Satelite Event: Modeling and Understanding Human Brain Computation at Scale
2026-08-02 10:00 - 18:00 | 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.

ARNI Continual Learning Working Group Meeting
2026-08-25 15:00 - 16:00 | Virtual
Our discussion will focus on the Day in the Life benchmark. We’re aiming to submit a proposal to the ICLR workshops once the timing is confirmed (and potentially an ICLR paper as well, with the deadline a month out), so we’d like to accelerate the push towards a working benchmark.
Zoom Link: Upon request @ arni@collumbia.edu

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
