I often heard statements like:
80% of your brain processing is computing the effect of gravity
You only use 20% of your brain power
My question isn't about the truth of these statements but if they are well-formed: can we measure 'processing' in the brain (or a sufficiently complicated artificial neural net) to quantify how much of the processing resources are allocated towards specific sub-routines?
As an example of when this is doable in another domain: consider a computer with a standard von Neumann architecture. In this case we can tell how much processing goes into sub-routine by simple counting the number of steps each subroutine holds the processor. However, this simple counting is only possible because of two features:
- There is a clear central processor, and data is not transformed by anything except it.
- There is a clear duality between software and hardware allowing us to identify when a specific sub-routine (software) is being allowed access to the processor (hardware).
Both features are missing in a computer with a neural architecture. How can we measure the resources that a computer with neural architecture (example: the brain) devotes to subtasks?
I am interested in settings where there isn't an obvious localization of sub-tasks. So an answer like:
we can measure the amount of resources that go towards recognizing faces by looking at the metabolic uptake of the fusiform face area.
Is not that interesting to me, since it is only valid for sub-tasks that happen to have specially-devoted areas. Of course, if there is an argument that any reasonable (or more restricted: human-like) neural-architecture has to compartmentalize its computations then I would like to hear that. However, the answerer would be left with the task of explaining how to identify the group of neurons that should be associated with a specific sub-task.