  Tasks that one wishes to have done by a computer often come with conditions
that relate to timescales. For instance, the processing must terminate within a
given time limit; or a signal processing computer must integrate input
information across several timescales; or a robot motor controller must react
to sensor signals with a short latency. For classical digital computing
machines such requirements pose no fundamental problems as long as the physical
clock rate is fast enough for the fastest relevant timescales. However, when
digital microchips become scaled down into the few-nanometer range where
quantum noise and device mismatch become unavoidable, or when the digital
computing paradigm is altogether abandoned in analog neuromorphic systems or
other unconventional hardware bases, it can become difficult to relate
timescale conditions to the physical hardware dynamics. Here we explore the
relations between task-defined timescale conditions and physical hardware
timescales in some depth. The article has two main parts. In the first part we
develop an abstract model of a generic computational system that admits a
unified discussion of computational timescales. This model is general enough to
cover digital computing systems as well as analog neuromorphic or other
unconventional ones. We identify four major types of timescales which require
separate considerations: causal physical timescales; timescales of phenomenal
change which characterize the ``speed'' of how something changes in time;
timescales of reactivity which describe how fast a computing system can react
to incoming trigger information; and memory timescales. In the second part we
survey twenty known computational mechanisms that can be used to obtain desired
task-related timescale characteristics from the physical givens of the
available hardware.
