BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARNI - ECPv6.18.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261019T150000
DTEND;TZID=America/New_York:20261019T160000
DTSTAMP:20261008T163210Z
CREATED:20260930T172828Z
LAST-MODIFIED:20261008T163210Z
UID:2660-1792422000-1792425600@arni-institute.org
SUMMARY:Speaker:Divyansha Lachi (UPenn) and Thorir Ingolfsson (ETH)– ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Divyansha’s talk\nTitle: Scaling Neural Motor Decoding via Decoupled Behavioral Pretraining\nAbstract:\nRecent neural decoding approaches achieve strong performance by training on large collections of paired neural-behavioral recordings\, but such datasets are expensive\, invasive\, and difficult to scale. In contrast\, behavioral data is abundant and easy to collect\, raising the question of whether neural decoding can benefit from decoupling behavioral and neural representation learning. We introduce BeeMO\, a flexible multi-session framework that enables training with arbitrary mixtures of paired neural-behavioral recordings and unpaired behavioral data. A behavior decoder learns movement dynamics directly from behavioral trajectories\, while neural activity is incorporated through cross-attention layers when available\, allowing behavioral and neural supervision to scale independently. By decoupling neural and behavioral data\, we unlock a new dimension for scaling: unpaired behavioral data\, which is substantially cheaper and easier to collect than paired neural recordings\, can be used directly to improve decoding performance. Across multiple intracortical motor datasets and tasks in nonhuman primates\, we show that incorporating unpaired behavioral data consistently improves decoding performance\, particularly […]\, and that incorporating task-aligned behavioral trajectories can further improve transfer. We further show that behavior-only pretraining\, without any neural pretraining\, outperforms single-session supervised baselines and is comparable to models pretrained on substantially larger neural datasets. Together\, our results suggest that scaling behavioral data offers a practical and cost-effective path toward neural decoding models that generalize across subjects and tasks. \nThorir’s talk\nTitle: Foundation Models for Wearables: From EEG and EMG to Multimodal Models at the Edge\nAbstract:\nWearable devices can continuously record EEG\, EMG\, ECG\, and PPG\, but labels are scarce\, electrode layouts vary across devices\, and inference must fit within a power budget of tens of milliwatts. This talk gives an overview of our work at ETH Zürich on foundation models built under these constraints. I will start with EEG: LUNA\, a topology-agnostic model that handles arbitrary electrode montages\, and FEMBA\, a state-space alternative to transformers that scales linearly with sequence length. I will then turn to the pre-training objective\, comparing masked reconstruction with latent-space prediction (LeJEPA) in LuMamba and examining what each learns. Next\, I will present TinyMyo\, a compact EMG foundation model that transfers across hand gesture classification\, hand kinematic regression\, and speech production and recognition\, and runs on an ultra-low-power microcontroller. Finally\, PanLUNA extends the approach to joint modeling of EEG\, ECG\, and PPG. \nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speakerdivyansha-lachi-upenn-and-thorir-ingolfsson-eth-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261020T020000
DTEND;TZID=America/New_York:20261020T160000
DTSTAMP:20260930T173946Z
CREATED:20260930T173946Z
LAST-MODIFIED:20260930T173946Z
UID:2665-1792461600-1792512000@arni-institute.org
SUMMARY:Speaker: Joshua Gold ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Joshua Gold \nTitle and Abstract: TBD \nZoom: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-joshua-gold-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Virtual
END:VEVENT
END:VCALENDAR