Ucnucs (Unconventional computing / natural computing scientists) party.

As detailed in a paper published this week in *Royal Society Open Science* 
<https://royalsocietypublishing.org/doi/10.1098/rsos.180396>, the amoeba 
used by the researchers is called *Physarum polycephalum*, which has been 
used as a biological computer in several other experiments 
The reason this amoeba is considered especially useful in biological 
because it can extend various regions of its body to find the most 
efficient way to a food source and hates light.

- pt

On Saturday, December 22, 2018 at 8:09:39 PM UTC-6, Brent wrote:
> Bruno should enjoy this. 
> Brent 
> -------- Forwarded Message -------- 
> This is a cool bio hack, but is this approach ever going to be faster 
> and/or cheaper than an electronic computer for the same precision of 
> optimization? 
> https://phys.org/news/2018-12-amoeba-approximate-solutions-np-hard-problem.html
> Amoeba finds approximate solutions to NP-hard problem in linear time 
> December 20, 2018 by Lisa Zyga, Phys.org 
> Researchers have demonstrated that an amoeba--a single-celled organism 
> consisting mostly of gelatinous protoplasm--has unique computing 
> abilities that may one day offer a competitive alternative to the 
> methods used by conventional computers. 
> The researchers, led by Masashi Aono at Keio University, assigned an 
> amoeba to solve the Traveling Salesman Problem (TSP). The TSP is an 
> optimization problem in which the goal is to find the shortest route 
> between several cities, so that each city is visited exactly once, and 
> the start and end points are the same. 
> https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180396 
> Remarkable problem-solving ability of unicellular amoeboid organism 
> and its mechanism 
> Choosing a better move correctly and quickly is a fundamental skill of 
> living organisms that corresponds to solving a computationally 
> demanding problem. A unicellular plasmodium of Physarum polycephalum 
> searches for a solution to the travelling salesman problem (TSP) by 
> changing its shape to minimize the risk of being exposed to aversive 
> light stimuli. In our previous studies, we reported the results on 
> the eight-city TSP solution. In this study, we show that the time 
> taken by plasmodium to find a reasonably high-quality TSP solution 
> grows linearly as the problem size increases from four to eight. 
> Interestingly, the quality of the solution does not degrade despite 
> the explosive expansion of the search space. Formulating a 
> computational model, we show that the linear-time solution can be 
> achieved if the intrinsic dynamics could allocate intracellular 
> resources to grow the plasmodium terminals with a constant rate, even 
> while responding to the stimuli. These results may lead to the 
> development of novel analogue computers enabling approximate solutions 
> of complex optimization problems in linear time. 

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