Biologically-inspired computing

Biologically-inspired computing (also bio-inspired computing) is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers. Biologically-inspired computing is a major subset of natural computation.

Areas of research
Some areas of study encompassed under the canon of biologically-inspired computing, and their biological counterparts:


 * genetic algorithms ↔ evolution
 * biodegradability prediction ↔ biodegradation
 * cellular automata ↔ life
 * emergent systems ↔ ants, termites, bees, wasps
 * neural networks ↔ the brain
 * artificial life ↔ life
 * artificial immune systems ↔ immune system
 * rendering (computer graphics) ↔ patterning and rendering of animal skins, bird feathers, mollusk shells and bacterial colonies
 * lindenmayer systems ↔ plant structures
 * communication networks and protocols ↔ epidemiology and the spread of disease
 * membrane computers ↔ intra-membrane molecular processes in the living cell
 * excitable media ↔ forest fires, the Mexican wave, heart conditions, etc
 * sensor networks

Bio-inspired computing and AI
The way in which bio-inspired computing differs from traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to the what could be described as 'creationist' methods used in traditional AI. In traditional AI, intelligence is often programmed from above: the programmer is the creator, and makes something and imbues it with its intelligence. Bio-inspired computing, on the other hand, takes a more bottom-up, decentralised approach; bio-inspired techniques often involve the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules, and a method of iteratively applying those rules. After several generations of rule application it is usually the case that some forms of complex behaviour arise. Complexity gets built upon complexity until the end result is something markedly complex, and quite often completely counterintuitive from what the original rules would be expected to produce (see complex systems).

Natural evolution is a good analogy to this method–the rules of evolution (selection, recombination/reproduction, mutation and more recently transposition) are in principle simple rules, yet over thousands of years have produced remarkably complex organisms. A similar technique is used in genetic algorithms.