Home > Biologically-inspired computing

1 Areas of research

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

• genetic algorithms ↔ evolution
• cellular automata ↔ life
• emergent systems ↔ ants, termites, bees, etc
• neural networks ↔ the brainFor other articles about other subjects named brain see brain (disambiguation). In the anatomy of animals, the brain or encephalon is the supervisory center of the nervous system. Although the brain is usually cited as the supervisory center of vertebrate
• artificial lifeArtificial life also known as alife is the study of life through the use of human-made analogs of living systems. Computer scientist Christopher Langton coined the term in the late 1980s when he held the first "International Conference on the Synthesis an ↔ life
• artificial immune systems ↔ immune systemThe immune system is any system present in an organism to prevent predation by biological agents. All living organisms have these protective measures, although they vary radically in scope and mechanism. In humans and domesticated animals, the immune syst
• computer renderingTraditionally, to render is to purify animal fats. For that topic, see rendering. See also Render. Rendering is the process of generating an image from a description of three dimensional objects, by means of a software program. The description is in a str ↔ patterning and rendering of animal skins, bird feathers, mollusk shells and bacterial colonies
• lindenmayer systems ↔ plant structures
• excitable mediaAn excitable medium is a nonlinear dynamical system which has the capacity to propagate a wave of some description, and which cannot support the passing of another wave until a certain amount of time has passed (known as the refractory time). A forest is ↔ forest fires, the Mexican wave, heart conditions, etc

2 Bio-inspired computing and AI

One way in which bio-inspired computing differs from 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.