Kalimános BT, Debrecen, 4032, Hungary.
Department of Neurology, Semmelweis University, 1085, Budapest, Hungary.
Acta Biotheor. 2022 Oct 26;70(4):26. doi: 10.1007/s10441-022-09450-6.
In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.
在各种计算的实现中，无论是技术的还是生物的，信息都存在于某种物质载体中，因此在实际实现中，信息的传播速度不能超过其载体的速度。由于这个限制，任何实现都必须考虑计算单元之间的传输时间。我们需要一种不同的数学方法来考虑这个限制：经典数学只能描述无限快速和小的计算系统的实现。数学处理方法的差异导致了对系统计算特性的不同描述。所提出的处理方法也解释了为什么生物实现可以进行终身学习，而技术实现却不能。我们关于学习的结论与已发表的生物和技术计算的实验证据相吻合。
