A gelatinous, single-celled life form has just solved an increasingly complex problem that many researchers use to test algorithms.

Even more impressive is the fact that, as the problem got harder, the slime mould amoeba actually solved the problem in a totally different – and arguably more efficient – way than most algorithms.

The result suggests that these simple lifeforms might actually offer an alternative processing method to conventional computers.

Or, to put it more simply, our state-of-the-art electronic devices could actually learn something from an amoeba. Ouch.

To be clear, the amoeba wasn’t faster than computers, not by a long stretch (check out how slow they move in the video below).

But while the problem got exponentially more complex, the amoeba’s processing time only increased linearly. You can see why that’s a big difference below:

The problem it had to solve was the Traveling Salesman Problem, or TPS for short. This is basically an optimisation problem which requires a computer to look at a list of cities and figure out the shortest route, so that each city is visited exactly once.

As more cities are added to the itinerary, the problem gets increasingly more complex – with four cities on the list there are only three possible routes to choose between. But for eight cities, the situation jumps up to 2,520 routes

In other words, it gets exponentially harder – and would take most systems a whole lot more time to figure out the best route.

But a team of researchers from Keio University in Japan decided to give the problem to a “true slime mould” amoeba Physarum polycephalum, and were surprised to find that as the cities increased from four to eight, the single-celled organism only needed a linear amount of more time to figure out a reasonable (almost optimal) route.

“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,” the researchers write in Royal Society Open Science

“These results may lead to the development of novel analogue computers enabling approximate solutions of complex optimisation problems in linear time.”

Of course, amoebas don’t know what cities are (as far as we know) so in this version of the TSP, the ‘cities’ were 64 narrow channels (eight ‘cities’ each containing eight channels) in a round plate placed on top of agar.

To get access to the agar and efficiently absorb nutrients, the amoeba enters the channels. 

The salesman ‘route’ it picks is its constantly changing body shape. So it makes one body shape when it enters one channel, a different body shape when it then enters a second channel, and so on. 

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