ARNI at COYSNE!

ARNI postdoctoral fellows Mehdi Azabou and Cole Hurwitz are among the co-organizers of the workshop "Building a Foundation Model for the Brain: Datasets, Theory, and Models" at COSYNE. This workshop is bringing together experts who are at the forefront of neuro-foundation model research. In addition, ARNI faculty: Blake Richards and Andreas Tolias are participating as speakers.

Workshop description: Advances in neurotechnology have enabled collection of large-scale neural recordings during animal behavior. To extract insights from these datasets, researchers are now faced with significant challenges when comparing data across different brain regions, subjects, and tasks. Inspired by recent successes in large-scale “foundation” modeling of natural language and computational biology, there has been a push towards developing “neurofoundation” models that can be trained across diverse datasets and then fine-tuned for downstream tasks and analyses including neural encoding, decoding, cell-type classification, and activity prediction. In this workshop, we aim to provide an overview of current neurofoundation model approaches and to bring together experimentalists, theoreticians, and model builders to discuss how this paradigm can lead to a deeper understanding of the brain. In particular, we plan to highlight the utility of neurofoundation models in analyses across recording modalities, brain regions, individuals, species, and contexts. In addition to these talks, we will also host a panel to discuss challenges, opportunities, and risks of building these models.

Columbia AI Summit

Columbia University's AI Summit on March 4th showcased the rich and interdisciplinary AI research taking place across its Morningside, Manhattanville, and Medical Center campuses. The event featured sessions and workshops spanning fields from healthcare to the humanities. The keynote address was delivered by Sami Haddadin, Director of the Munich Institute of Robotics and Machine Intelligence and Vice President for Research at MBZUAI.

ARNI's Director, Richard Zemel, led the opening panel session on "New Frontiers of AI." Alongside him, ARNI faculty members Christos Papadimitriou and Elias Bareinboim delivered lightning talks, exploring the foundational principles of AI advancements and their broad impact on industry and society. Professor Zemel also welcomed students and faculty attending the School of Engineering's Demo Session, which featured cutting-edge research, including examples of motor learning and embodied intelligence on dexterous robot hands.

ARNI Distinguished Seminar Speaker: Dr. Marlene Behrmann

On February 28th, Dr. Marlene Behrmann gave a talk titled "The Development, Hemispheric Organization, and Plasticity of High-Level Vision," offering new insights into how the brain processes faces and words. While traditional research suggested a strict left-right hemisphere division—words in the left, faces in the right—Dr. Behrmann’s findings reveal a more flexible and distributed organization that differs across individuals. Her work highlights the brain’s adaptability, challenging long-held assumptions and advancing our understanding of visual processing.

Bio:
Dr. Marlene Behrmann joined the Department of Ophthalmology at the University of Pittsburgh School of Medicine, where she holds the John and Clelia Sheppard Chair, in 2022. She also holds the position of Emeritus Professor at Carnegie Mellon University. Dr. Behrmann’s research is concerned with the psychological and neural bases of visual processing, with specific attention to the mechanisms by which the signals from the eye are transformed into meaningful percepts by the brain

ARNI's Research Highlight

ARNI Working Groups

• Summary: Neural network models are typically designed with a fixed architecture, determining the number of nodes, connectivity, and timesteps for backpropagation. While this approach helps limit resource requirements and optimize performance, it restricts the ability to explore tradeoffs between space, time, energy, and error. To address this, we aim to develop methods for quantifying resource costs and optimizing models to balance multiple constraints efficiently, benefiting both neuroscience and AI development.
• On going working group led by Nikolaus Kriegeskorte's Group
• Contact Eivinas Butkus [email protected] or Josh Ying [email protected] for more information 

Join a global community of experts and peers, enhance your problem-solving abilities, and build a supportive network that champions accessibility to quality science education for everyone. This course explores key system features that impact generalization, including task structure, microcircuitry, macrocircuitry or architecture, learning rules, and data streams. Don’t miss this unique opportunity to expand your knowledge and skills.

Course Details:
Course Name: Neuromatch Academy NeuroAI Course
Dates: July 14th – 25th, 2025
Course Format: Synchronous virtual
Applications Close: Sunday, March 23rd, midnight in your time zone

Requirements:
Students are expected to be familiar with Python and have foundational science skills in math, in addition to completing NMA CN and DL courses or equivalent. This is an advanced course.
Teaching Assistants should have strong Python skills and a background in neuroscience and AI.

Apply Now: https://lnkd.in/eMiiBGS

Should you have any questions or need further information, feel free to reach out to Neuromatch Academy at [email protected].

We would like to highlight ARNI achievements in future newsletters. Please share with us your events, papers, presentation, or any news you want to share with the ARNI community!