NeuroAI and Beyond

TL;DR

This paper discusses the emerging field of NeuroAI, which integrates neuroscience insights into AI development, highlighting current progress, future directions, and the potential for mutual advancement in understanding neural computation and improving AI systems.

Contribution

It provides a comprehensive overview of NeuroAI, proposing it as a promising interdisciplinary approach that enhances AI capabilities and deepens understanding of biological neural processes.

Findings

NeuroAI has potential to significantly improve AI algorithms.

Interdisciplinary collaboration is crucial for advancing NeuroAI.

SWOT analyses reveal key benefits and risks of NeuroAI.

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.