[visionlist] AAAI 2026 workshop "Neuro for AI & AI for Neuro: Towards Multi-Modal Natural Intelligence"

[Anton Arkhipov]

Dear Colleagues,

Join us for the Workshop “Neuro for AI & AI for Neuro: Towards Multi-Modal Natural Intelligence” at the AAAI 2026 conference in Singapore on January 27, 2026.

We are bringing together researchers in neuroscience and AI to explore how brain principles can inspire multimodal models, and how AI can push the frontiers of neuroscience. Please see details below and at https://neuroai-multimodal-workshop.github.io/.

We invite submissions of full papers or abstracts that describe new research, work in progress, or position statements on relevant topics. The full paper submissions should be 8 pages maximum, excluding references, and the abstract submissions should be 2 pages maximum, excluding references, in the AAAI two-column format.
Please submit your work by October 30, 2025 at https://openreview.net/group?id=AAAI.org/2026/Workshop/NeuroAI.

Invited speakers include

  *   Dmitri "Mitya" Chklovskii (Flatiron Institute, NYU)
  *   Andreas Tolias (Stanford University)
  *   Martin Schrimpf (EPFL)
  *   Guozhang Chen (Peking University)
  *   Adeel Razi (Monash University)
  *   Mike Zheng Shou (National University of Singapore)

Organizing Committee:

  *   Reza Abbasi-Asl, University of California, San Francisco
  *   Asim Iqbal, Tibbling Technologies / Weill Cornell Medicine
  *   Sophia Sanborn, Stanford University
  *   Shinya Ito, Allen Institute
  *   Anton Arkhipov, Allen Institute

Description of workshop: This workshop will unite researchers in artificial intelligence, computational neuroscience, and neuromorphic engineering to explore the critical two-way exchange between biology and machine learning. Our main objective is to bridge these fields to accelerate foundational progress in multimodal natural intelligence. The “Neuro → AI” theme will investigate how principles from cortical microcircuits, such as sparse coding, dendritic nonlinearities, and cell-type diversity, can inspire novel, efficient, and robust AI architectures like transformers and graph-based models. Conversely, the “AI → Neuro” theme will showcase how advanced machine-learning techniques are revolutionizing neuroscience, enabling new insights into neural dynamics through AI-driven analysis of large-scale imaging, electrophysiological, and behavioral data. By bridging the gap between biologically grounded inductive biases for AI and scalable computational tools for neuroscience, this workshop aims to catalyze research that unifies theory, experiment, and application.

Topics:

  *   Biologically inspired machine learning architectures
  *   Neuro-inspired mechanisms for efficiency (e.g., sparsity, inhibitory-excitatory balance)
  *   AI-driven analysis of large-scale neuroscience data
  *   Unsupervised representation learning and causal inference for circuit discovery
  *   Gradient descent-based training of bio-realistic neural models
  *   Open-source software and reproducible research in NeuroAI
  *   Neuromorphic engineering and hardware implementation

Format of Workshop: This will be a one-day, in-person workshop. The format is designed to be highly interactive, featuring a series of invited talks from world-renowned experts, two panel discussions for in-depth Q&A, and spotlight presentations for high-impact contributed papers. A central poster session will provide a forum for detailed discussion and networking. The day will conclude with a roundtable discussion to identify key community challenges and a sponsored social gathering to foster continued collaboration.

Attendance: We welcome researchers and practitioners from academia and industry with an interest in the intersection of neuroscience and artificial intelligence. At least one author of each accepted submission must be present at the workshop. The maximum number of attendees is to be determined by the room size and will be communicated by AAAI.

Please reach out if you have any questions. We look forward to seeing you in Singapore!

Best regards,

Reza Abbasi-Asl, Asim Iqbal, Sophia Sanborn, Shinya Ito, and Anton Arkhipov.


Anton Arkhipov
Investigator, Allen Institute
antona at alleninstitute.org<mailto:antona at alleninstitute.org>
alleninstitute.org<https://alleninstitute.org>
brain-map.org<https://portal.brain-map.org/>

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