The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training while still struggling to compete in tasks that are trivial for biological agents. Thus, brain-inspired engineering has emerged as a promising new avenue for designing sustainable, next-generation AI systems. Here, we describe how dendritic mechanisms of biological neurons have inspired innovative solutions for significant AI problems, including credit assignment in multilayer networks, catastrophic forgetting, and high energy consumption. These findings provide exciting alternatives to existing architectures, showing how dendritic research can pave the way for building more powerful and energy-efficient artificial learning systems.

翻译：大脑是一个能力卓越且高效的系统。它能够利用极少的能量处理并存储大量嘈杂且非结构化的信息。相比之下，当前的人工智能（AI）系统在训练时需要消耗大量资源，却在生物智能体轻松完成的任务上仍难以匹敌。因此，受大脑启发的工程学已成为设计可持续、下一代AI系统的一条有前景的新途径。本文阐述了生物神经元的树突机制如何为重大AI问题提供创新解决方案，包括多层网络中的信用分配、灾难性遗忘以及高能耗问题。这些发现为现有架构提供了令人兴奋的替代方案，展示了树突研究如何为构建更强大、更节能的人工学习系统铺平道路。