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English(EN) Universal Construction and Exact Self-Reproduction in Ternary McCulloch-Pitts Networks

研究人员在神经网络中实现精确自复制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个固定的McCulloch-Pitts阈值单元网络，能够执行和复制其状态内的其他阈值网络。该系统受John von Neumann工作的启发，实现了包括权重和内存的网络精确自复制。该方案可扩展为通用计算机，通过一个SUBLEQ计算机得到证明，该计算机可以构建和复制适合其存储库的任何网络，并能发出自身的序列化。 AI

影响 这项理论工作探索了神经网络中计算和自复制的基本原理，可能影响未来的AI架构。

排序理由 该集群包含一篇详细介绍神经网络新颖理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究人员在神经网络中实现精确自复制

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该集群包含一篇详细介绍神经网络新颖理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Charles C. Norton ·

    通用构造与精确自复制在三元 McCulloch-Pitts 网络中的应用

    A fixed network of McCulloch-Pitts threshold units with weights in {-1,0,1} can hold other threshold networks in its state and run them: the state is a ring of banks of records, each record a unit of a stored network, and each step evaluates one record. We use such a network to c…

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