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X-WR-CALDESC:Events for ARNI
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DTSTART;TZID=America/New_York:20260605T160000
DTEND;TZID=America/New_York:20260605T170000
DTSTAMP:20260609T160425
CREATED:20260604T201257Z
LAST-MODIFIED:20260604T201341Z
UID:2549-1780675200-1780678800@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings \nZoom: request @ arni@columbia.edu
URL:https://arni-institute.org/event/select-arni-biological-learning-working-group/
LOCATION:Virtual
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260608T150000
DTEND;TZID=America/New_York:20260608T160000
DTSTAMP:20260609T160425
CREATED:20260601T193651Z
LAST-MODIFIED:20260601T193651Z
UID:2523-1780930800-1780934400@arni-institute.org
SUMMARY:Speaker: Mia Dai – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Speakers: Mia Dai\, PhD student in the Paninski Group at Columbia University \nTitle: Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding \nAbstract: 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. \nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-mia-dai-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260626T113000
DTEND;TZID=America/New_York:20260626T130000
DTSTAMP:20260609T160425
CREATED:20260501T143846Z
LAST-MODIFIED:20260501T143846Z
UID:2482-1782473400-1782478800@arni-institute.org
SUMMARY:CTN: Bence Olveczky (Harvard University) - June 26\, 2026
DESCRIPTION:Bence Olveczky \nLocation: ZI\nDate: June 26\, 2026\nTime: 11:30am\nZoom: Upon Request @ arni@Columbia.edu \nTitle and Abstract: TBD
URL:https://arni-institute.org/event/ctn-bence-olveczky-harvard-university-june-26-2026/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260629T150000
DTEND;TZID=America/New_York:20260629T160000
DTSTAMP:20260609T160425
CREATED:20260608T153559Z
LAST-MODIFIED:20260608T154659Z
UID:2550-1782745200-1782748800@arni-institute.org
SUMMARY:Speaker: Konstantinos Barmpas (Imperial College London) and Jarod Lévy (Meta FAIR Paris and Inria MIND) – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Konstantinos Barmpas (Postdoc Imperial College London)\nTitle: NeuroRVQ – Multi-Scale Biosignal Tokenization for Generative Foundation Models\nAbstract: 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.\n\n\nJarod Lévy (PhD Student Meta FAIR/Inria)\nTitle: DANCE – Detect and Classify Events in EEG\nAbstract: 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.\n\nZoom: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-konstantinos-barmpas-and-jarod-levy-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260909T150000
DTEND;TZID=America/New_York:20260909T160000
DTSTAMP:20260609T160425
CREATED:20260501T134305Z
LAST-MODIFIED:20260527T143526Z
UID:2476-1788966000-1788969600@arni-institute.org
SUMMARY:Speaker: Alexandre Pouget - ARNI Distinguish Seminar Series
DESCRIPTION:Alexandre Pouget \nDate and Time: May 21st at 3pm \nLocation: Zuckerman Institute Kavli Auditorium 9th Floor \nTitle: Neural Models of Compositionality \nAbstract: 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. \nZoom link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-alexandre-pouget-arni-distinguish-seminar-series/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
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