仿生计算系统中的时空行为
On the spatiotemporal behavior in biology-mimicking computing systems
摘要：
从单处理器到超级计算机，传统计算系统的有效载荷性能达到了自然允许的极限。对处理“大数据”（基于或由人工智能辅助）的需求不断增长，对更全面地了解我们大脑的运作的兴趣也不断增长，这两种需求刺激了人们努力用廉价的传统组件构建模仿生物的计算系统，并构建不同的（“神经形态”）计算系统。一方面，这些系统需要异常大量的处理器，这引入了性能限制和非线性缩放。另一方面，神经元的操作与传统的工作量有很大的不同。传统的计算（包括其数学背景和物理实现）是基于假设的即时交互，而生物神经元系统具有“时空”行为。仅这一差异就使得在技术实现中模仿生物行为变得困难。此外，近年来计算领域的一些问题使人们注意到，时间行为也是计算系统的一个普遍特征。它们在生物和技术系统中的一些影响已经被注意到。然而，对这些问题的处理是不完整/不适当的。引入基于Minkowski变换的时态逻辑，定量地洞察了这两种计算系统的操作，并为几十年前的经验现象提供了自然的解释。如果不正确地考虑它们的时间行为，就不可能有效地实现或真正地模仿生物神经系统。
英文摘要：
The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive conventional components and build different ("neuromorphic") computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically differs from the conventional workloads. The conventional computing (including both its mathematical background and physical implementation) is based on assuming instant interaction, while the biological neuronal systems have a "spatiotemporal" behavior. This difference alone makes imitating biological behavior in technical implementation hard. Besides, the recent issues in computing called the attention to that the temporal behavior is a general feature of computing systems, too. Some of their effects in both biological and technical systems were already noticed. Nevertheless, handling of those issues is incomplete/improper. Introducing temporal logic, based on the Minkowski transform, gives quantitative insight into the operation of both kinds of computing systems, furthermore provides a natural explanation of decades-old empirical phenomena. Without considering their temporal behavior correctly, neither effective implementation nor a true imitation of biological neural systems are possible.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
