This paper makes a solid point. The argument about whether a brain is like a microprocessor or not (obviously not) is a distraction to the main point, which is that without understanding the flow of information we are simply sorting through patterns in the data coming out of neuroscience without coming to any real understanding.
For instance, it's quite surprising how much effort is spent on measuring and tracking different oscillations in the brain. These are correlative with respect to the actual information flows, but not the same thing as the latter. We rather need to tease apart the mechanisms with which the constitutive neurons communicate with each other, in various behavioural and functional contexts. From there, we may then find how the oscillations arise, e.g., they are a byproduct of particular forms of processing. Instead, we put the cart before the horse and focus excessively on the oscillations first (or any large scale activity), before examining cell specific electrophysiology and neurochemical interactions. Indeed, many neuroscientists are loathe to go that "low level"!
This results in a state of affairs in neuroscience (at least systems level) where we are content with scratching the surface, e.g., measure changes in frequencies under different conditions. It's like measuring the spectral signature that the sound of a car's engine makes, and thinking that by comparing the peak power under idle vs driving conditions we are any closer to understanding how the engine works. However, we are actually in a very primitive state of "understanding". We don't know anything about pistons and drivetrains, let alone the principles of internal combustion. Yet it's only by understanding these that we would truly know how a car engine works. The same applies to the brain.