用高维向量模拟生物学习，实现类脑自主计算。
Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's
- 以高维向量代替数字运算，模仿神经系统的并行处理机制。
- 架构包含高容量向量内存，类比传统计算机的RAM，支持长期记忆存储。
- 融合心理学与神经科学，适用于机器人学习与未来类脑计算系统。
我们通过高维向量（例如 H = 10,000）建模人类和动物的学习过程。该架构类似于传统的冯·诺依曼计算机，但指令作用于向量并在叠加态中操作。系统包含一个高容量向量存储器，相当于数字计算机中的随机存取存储器（RAM）。该模型学习能力类似深度学习，但架构更贴近生物学。其结构符合心理学观点：人类记忆与学习涉及短期工作记忆与长期数据存储。神经科学提供长期记忆的模型，即小脑皮层。结合心理学、生物学与传统计算，向量计算理论有助于理解大脑如何运作。应用于机器人学习势在必行，未来或拓展至语言处理。最终目标是实现与大脑相当的材料与能耗效率。为此，需建立符合心理学与生物学的数学理论，并适合纳米技术实现。还需在大规模实验中验证该理论。
原文摘要 · Abstract (English)
We model human and animal learning by computing with high-dimensional vectors (H = 10,000 for example). The architecture resembles traditional (von Neumann) computing with numbers, but the instructions refer to vectors and operate on them in superposition. The architecture includes a high-capacity memory for vectors, analogue of the random-access memory (RAM) for numbers. The model's ability to learn from data reminds us of deep learning, but with an architecture closer to biology. The architecture agrees with an idea from psychology that human memory and learning involve a short-term working memory and a long-term data store. Neuroscience provides us with a model of the long-term memory, namely, the cortex of the cerebellum. With roots in psychology, biology, and traditional computing, a theory of computing with vectors can help us understand how brains compute. Application to learning by robots seems inevitable, but there is likely to be more, including language. Ultimately we want to compute with no more material and energy than used by brains. To that end, we need a mathematical theory that agrees with psychology and biology, and is suitable for nanotechnology. We also need to exercise the theory in large-scale experiments. Computing with vectors is described here in terms familiar to us from traditional computing with numbers.
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