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Digital computer being compared to the human brain must be normalized and/or specified. Eg, 32 bit and 64 bits have different power; supercomputers and desktops , can successfully compute a different set of functions (even regardless of time taken).
Darendra Modhi, lead of IBM's SyNAPSE project, estimates the brain has 38 petaflops of computing power . 
TaihuLight, the world's most powerful supercomputer, now has 93 petaflops.  They have radically different energy requirements and operate at radically different speeds.
Definition of computational power:
the ability to compute a function.
there is a function that A can compute and B can't => A and B don't have the same computational power. (A also can compute every function that B can compute => A has more computational power than B)
Your definition of power fails to take into account the speed of the processing. What would take a computer milliseconds would take a human an hour.
it is the definiton in the Theory of Computer Science point of view.
you can't argue against a definiton (if it is the definition of the OP )
It depends on what you mean by computational power.
i gave a definition
both are Turing complete
(assuming the brain has acces to external memory like a pen and paper)
Given a fixed (finite) amount of time, both theoretical Turing machine , digital computer, brain can only manipulate a finite amount of data.
Given this, all Turing complete machines != equivalent computational power (per paren'ts definition).
A more accurate model of computational power for modern generation digital computers is the Random-access stored-program machine . Analog, recurrent neural networks may be represented by non-deterministic TM, or something even more complex  > different power than RASP.