[arc619]

I think the article's overall stride is not so much about how these things are represented in neuronal mapping per se, and more that we shouldn't apply the idea of computer mechanics to organisms.

Less load/process/store of absolute data and more like natural processes such as the process of erosion creating rivers. The analogy of the environment as a "lock" and organisms are just the most fit "keys" to success in particular environment.

So the computer analogy is bad because organisms are more a matrix of interactions, feedbacks and responses that work well enough, but dont follow a "logical" design. This can be replicated within a computer easily and the result is evolutionary computation & hardware, genetic algorithms and evolutionary neural networks. The problem in understanding the result of evolutionary systems is that they're blind to design and only respond to fitness and therefore create systems that are so tightly coupled it's a quest to understand how the model even works.

So the article is suggesting we shouldn't apply human design principles to evolved solutions. Perhaps we need some kind of "messy science" to make sense of it all.

Going forward, machine learning running evolutionary algorithms on neural networks should be able to produce sufficiently incomprehensibility for us to be studying our own inventions for years to come.