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 multi-layer networks, catastrophic forgetting, and high-power 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.

中文翻译：

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利用树突特性推进机器学习和神经启发计算

大脑是一个非常有能力和高效的系统。它可以使用最少的能量处理和存储大量噪声和非结构化信息。相比之下，当前的人工智能 (AI) 系统需要大量资源进行训练，同时仍难以完成对生物制剂而言微不足道的任务。因此，类脑工程已成为设计可持续的下一代人工智能系统的有前途的新途径。在这里，我们描述了生物神经元的树突机制如何激发了重大人工智能问题的创新解决方案，包括多层网络中的信用分配、灾难性遗忘和高功耗。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。