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我最近在思考当前深度学习的核心低效时，了解到神经形态计算。如今，人工智能在很大程度上依赖于超密集的层次、各层和神经元的持续计算，以及全局反向传播来寻找最佳的权重更新。然而，当你观察人脑时，发现这些并不存在。大脑的运作原则与现代大型语言模型（LLM）形成鲜明对比：- 局部演化：神经元在很大程度上是独立的，基于其局部环境和简单的反馈回路（如神经递质，例如多巴胺）而演化，而不是依赖于全局误差信号。 - 极端稀疏性：系统极其稀疏（神经元只有在参与发放链时才会演化和更新）。 - 事件驱动处理：神经元只有在被评估时才会发放，并且实际上是被其他发放的神经元触发的。相比之下，我认为当前的LLM似乎是通过同时训练所有层和神经元，强行解决问题，并试图找到最佳的全局更新函数。如果你观察过去几年人工智能进步的轨迹，会发现一个明显的模式：- 2020–2022：扩大数据集和原始计算能力。 - 2023–2024：扩展上下文窗口并转向专家混合模型（MoE）。 - 2024–2025：链式思维和推理时的推理。 - 2025年至今：自主执行和并行多智能体系统。从根本上说，这些进展无一例外地只是扩大计算和处理令牌的不同方式。因此，这种扩展在某个时刻自然会遇到瓶颈，因为电力并不是无限的。这就是为什么我相信，长期的进步不能仅仅依靠无止境的扩展。我们需要的是根本性的效率提升。那么，神经形态处理是否正是这种解决方案，还是说它尚未成熟呢？

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I’ve recently learned about neuromorphic computing while thinking about the core inefficiencies of current deep learning.Today AI relies heavily on super-dense layers, continuous computation of all layers and neurons at all times, and global backpropagation to find optimal weight updates. But when you look at the human brain, none of that happens.The brain operates on principles that stand in contrast to modern LLMs:- Local Evolution: Neurons are largely independent, evolving based on their local neighborhood and simple feedback loops like neurotransmitters (e.g., dopamine) rather than a global error signal. - Extreme Sparsity: The system is massively sparse (neurons only evolve and get updated when they have been involved in a firing-chain). - Event-Driven Processing: Neurons are only firing when they get evaluated and are actually triggered by other spiking neurons.In contrast, current LLM‘s seem to me like they brute-force their way through problems by training all layers and neurons at the same time and trying to find the optimal global update function.If you look at the trajectory of AI advancements over the last few years, a clear pattern emerges:- 2020–2022: Scaling up datasets and raw compute. - 2023–2024: Expanding context windows and shifting to Mixture of Experts (MoE). - 2024–2025: Chain-of-thought and inference-time reasoning. - 2025–Present: Autonomous execution and parallel multi-agent systems.Fundamentally, every single one of these advancements is just a different way of scaling up compute and processed tokens. So this scaling will naturally hit a wall at some point as electricity is not unlimited. That’s why I believe that long term progress can not come from just scaling forever. What it needs is radical efficiency improvements. So could neuromorphic processing be exactly that or is it not mature yet?

请问HN：神经形态计算会取代传统人工智能吗？