This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early connectionist models to state-of-the-art large language models, demonstrating how key innovations like transformer attention, foundation-model pre-training, and multi-agent architectures mirror neurobiological processes like cortical mechanisms, working memory, and episodic consolidation. We then discuss emerging physical substrates capable of breaking the von Neumann bottleneck to achieve brain-scale efficiency in silicon: memristive crossbars, in-memory compute arrays, and emerging quantum and photonic devices. There are four critical challenges at this intersection: 1) integrating spiking dynamics with foundation models, 2) maintaining lifelong plasticity without catastrophic forgetting, 3) unifying language with sensorimotor learning in embodied agents, and 4) enforcing ethical safeguards in advanced neuromorphic autonomous systems. This combined perspective across neuroscience, computation, and hardware offers an integrative agenda for in each of these fields.

翻译：这篇立场与综述论文指出了神经科学、通用人工智能（AGI）与神经形态计算正汇聚成一个统一的研究范式。基于大脑生理学框架，我们重点阐释了突触可塑性、稀疏脉冲通信与多模态关联如何为下一代可能融合人类与机器智能的AGI系统提供设计原则。本文追溯了从早期联结主义模型到最先进大型语言模型的演变过程，展示了Transformer注意力机制、基础模型预训练和多智能体架构等关键创新如何镜像神经生物学过程——包括皮层机制、工作记忆与情景记忆巩固。随后讨论了能够打破冯·诺依曼瓶颈、在硅基实现大脑级效率的新兴物理基底：忆阻交叉阵列、存内计算阵列以及新兴的量子与光子器件。在这个交叉领域存在四个关键挑战：（1）将脉冲动力学与基础模型相融合；（2）在不发生灾难性遗忘的前提下保持终身可塑性；（3）在具身智能体中统一语言与感觉运动学习；（4）在先进的神经形态自主系统中落实伦理安全措施。这种融合神经科学、计算理论与硬件技术的综合视角，为上述每个领域提供了一份整合性研究议程。