  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.
