NeuroAI and Beyond

2026-01-27

AI Summary

This study addresses the longstanding lack of deep integration between artificial intelligence and neuroscience, which has hindered both algorithmic efficiency and our understanding of biological intelligence. The authors propose and advocate for a “NeuroAI” paradigm grounded in bidirectional synergy: on one hand, leveraging neuroscientific principles—such as embodied cognition and neuromorphic computing—to advance next-generation AI systems; on the other, employing AI models to enhance the interpretation of biological neural computation. Through a systematic review of the current landscape, synthesis of expert perspectives, and a SWOT analysis, the work identifies concrete collaborative pathways in domains including language communication, robotics, and human-AI co-learning, thereby offering a clear roadmap for future interdisciplinary research at the intersection of neuroscience and artificial intelligence.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Intelligent Robots: Embodied AI
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Neuroscience and Artificial Intelligence (AI) have made significant progress in the past few years but have only been loosely inter-connected. Based on a workshop held in August 2025, we identify current and future areas of synergism between these two fields. We focus on the subareas of embodiment, language and communication, robotics, learning in humans and machines and Neuromorphic engineering to take stock of the progress made so far, and possible promising new future avenues. Overall, we advocate for the development of NeuroAI, a type of Neuroscience-informed Artificial Intelligence that, we argue, has the potential for significantly improving the scope and efficiency of AI algorithms while simultaneously changing the way we understand biological neural computations. We include personal statements from several leading researchers on their diverse views of NeuroAI. Two Strength-Weakness-Opportunities-Threat (SWOT) analyses by researchers and trainees are appended that describe the benefits and risks offered by NeuroAI.

Problem

• NeuroAI
• Neuroscience
• Artificial Intelligence
• Interdisciplinary Synergy
• Neuromorphic Engineering

Innovation

• NeuroAI
• neuroscience-informed AI
• embodiment
• neuromorphic engineering
• human-machine learning