Biological networks utilize similar algorithms as engineered counterparts
1–10 of 13 posts
Re: Biological networks utilize similar algorithms as engineered counterparts
#2I 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.
Re: Biological networks utilize similar algorithms as engineered counterparts
#3I 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.
I think that the phrase "utilize similar" does not imply an order.
Anyway, I'm curious what would be the engineering counterpart of caffeine :)
Re: Biological networks utilize similar algorithms as engineered counterparts
#4I 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.
I think that the phrase "utilize similar" does not imply an order. Anyway, I'm curious what would be the engineering counterpart of caffeine :)
caffeine is the engineering counterpart of caffeine.
Re: Biological networks utilize similar algorithms as engineered counterparts
#5Rather 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!
[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
Re: Biological networks utilize similar algorithms as engineered counterparts
#6Could also be titled, "humans continuously reinvent the wheel, take credit anyway."
Re: Biological networks utilize similar algorithms as engineered counterparts
#7Could also be titled, "humans continuously reinvent the wheel, take credit anyway."
Let's just hope God doesn't patent any of it all.
Re: Biological networks utilize similar algorithms as engineered counterparts
#8Re: Biological networks utilize similar algorithms as engineered counterparts
#9(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.
Re: Biological networks utilize similar algorithms as engineered counterparts
#10Apropos 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."
dx.doi.org/10.1073/pnas.0914771107 https://pdfs.semanticscholar.org/1e5a/bf57c88ad060046c5b2adc...