Rather interestingly, you see this sort of mirroring in other areas of neuroscience as well. One example is the simple cell receptive fields in the visual cortex [0]. If you take a natural image, slice it up into a bunch of small patches, and run independent components analysis (ICA) on them [1], you end up with patches that look a lot like simple cell receptive fields, implying that the visual system uses something very similar to ICA to process information.
In the same way, you can run ICA on human speech, and what you get back are gammatone filters, [2] which are commonly used to model the auditory system!
I think you got the headline backwards- engineered systems use system algorithms to biological networks. Since after all, those biological systems have been doing these sorts of things, without being engineered.
That we rediscover biological mechanisms present in our own designs, we should not be surprised.
Apropos the 2010 PNAS article "Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks"
Abstract: "The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions."
(mystified expression) ... translation: "Biological networks use algorithms similar to engineered counterparts". But as another correspondent has pointed out, it's really the other way around.
But similarity is a symmetric thing, like the relation "close to". So X is similar to Y implies Y is similar to X. And if the biological knowledge is a more recent discovery that the engineering knowledge...
Yes, I agree with the idea, but the sense of "use ... similar to ..." implies intent and temporal order in many contexts. If I use language similar to Ernest Hemingway, am I justified in expressing it in the reverse order?
More realistically, in copyright and patent disputes, saying a person has "used ... (a method) similar to" that of a presumed originator matters a great deal, and a defendant in such an action may well reverse the order of the words in his own defense -- "I didn't use a method similar to Mr. Smith's, he used a method similar to mine." Clearly there's a temporal order implied in this particular context.
> And if the biological knowledge is a more recent discovery that the engineering knowledge...
Good point. One might assume biology is farther along in its grasp of the intellectual terrain than engineering, but that's not necessarily true.
It's only within a cultural context that it seems odd to liken a less famous writer to a more famous one. For an outsider it would be unremarkable and possibly helpful.
I think the feeling about comparing yourself with Hemingway, or Hemingway with yourself (note: comparison does have an asymmetric feeling for me) may arise from the fact that you don't really believe your (hypothetical, putative) writing is similar to Hemingway's on the crucial dimension of quality. So it naturally jars to think of Hemingway's writing defined as similar to it.
The words of the defendant in your example actually read to me more like a Groucho Marx gag than a credible defense, but I'm in Ireland where usage may differ.
In the same way, you can run ICA on human speech, and what you get back are gammatone filters, [2] which are commonly used to model the auditory system!
[0]: https://en.wikipedia.org/wiki/Simple_cell [1]: https://en.wikipedia.org/wiki/Independent_component_analysis [2]: https://en.wikipedia.org/wiki/Gammatone_filter