No sooner did I mention an apparent trend toward using neural networks to analyze biological processes than another fascinating study came up on my radar: in Switzerland two engineers and a biologist are using a neural network to simulate the biological evolution of social communication.
The Swiss research employs camera-equipped robots instead of animals, and it sends a group of robots out "foraging." The robots are equipped with light sensors, and the "food" at one end of the foraging area is a lighter color than the "poison" at the other end. The bots are scored for their success at locating the "food," and they "talk about" their success by flashing a blinking blue light. Which, in turn, draws robots that are computationally attracted to the blue light.
After that, the self-prepetuating algorithms of the neural network experiment begin to look a little like genetic algorithm optimization. The robots are randomly "mated," and their neural nets are mingled. In only a few generations, the robots learn to head towards the blue lights. But here there is a hitch: the "food source" is available to only a few robots at a time.
What evolves next? By the 50th generation the robots begin to do what you and I would probably do if we had the advantage of a precious resource we didn’t want to share: they deceive. They stop signaling with their blue lights when they find "food" because their signals will draw a crowd of greedy robots.
Interestingly, this story of evolving robots mirrors research done on live chickens in the mid-1990s, except that there was also sex and mating motivation–the real kind–at work in those experiments. Animal behaviorists studying vocal communication observed that roosters will call to notify other chickens about the availability of food only if those other chickens are hens.
In spite of this difference, the evolutionary logic of both the chickens and the robots is the same: why share the good stuff and lose your advantage?