One of the goals of the NIH BRAIN (Brain Research Through Advancing Innovative Neurotechnologies) Initiative is to develop theories, models, and methods to understand brain functions and their causal links to behaviors. The modern advances in neuroscience and AI have generated growing interests in NeuroAI, as witnessed by the feedback from the recent NIH BRAIN NeuroAI workshop (Nov. 12-13, 2024). Briefly, NeuroAI is aimed to, first, use AI to understand and improve the brain and behaviors, and second, to develop brain-inspired AI systems for robust, faster and more efficient operations and performances. Motivated by the new wave and developments in NeuroAI, this workshop invites leading experts and new investigators from various research backgrounds to discuss many emerging research topics. The goal of this full-day workshop, in the name of BRAIN 2.0 (BRidging AI and Neuroscience), is to focus on building the bridge between AI and neuroscience, to discuss new research directions and outstanding questions, and to foster team collaborations and open science. Research topics of interest include but not limited to neural transformers and foundation models, new neural network architectures, distributional or meta reinforcement learning, structural reasoning and inference, large language models (LLMs), and digital twins brain. The format of the workshop will consist of both overview-like and research-oriented lecture presentations as well as panel discussions.
Confirmed speakers (in alphabetical order) include:
Z. Sage Chen, Multiplicative couplings facilitate learning and flexibility in recurrent neural circuits
Rui Ponte Costa, Learning across the brain: a NeuroAI perspective
Tatiana Engel, Closing the discovery loop with digital twins and causal perturbations Michael M. Halassa, Thalamocortical architectures for cognitive control and flexibility Daniel Levenstein, NeuroAI as a platform for theory development: lessons from hippocampal representation and replay Kevin Miller, Automatically discovering neuroscience theories from data Gaspard Oliviers, Bidirectional predictive coding: Towards robust inference and versatile learning in the brain Seng Song, Brain inspired learning rules and evolutionary computing Naoshige Uchida, Distributional reinforcement learning in the brain
