Bio-inspired computing: Difference between revisions

Browse history interactively ← Previous edit Next edit →Content deleted Content addedVisual WikitextInline

Revision as of 12:01, 22 September 2012 editQuipa (talk \| contribs)357 edits "Nature" substituted by "life". "Nature" is a broad term that includes biology. Bio-inspired should be related to life, not to the inanimate world.← Previous edit		Revision as of 08:50, 11 February 2013 edit undoBeno1000 (talk \| contribs)Pending changes reviewers3,659 editsmNo edit summaryNext edit →
Line 1:		Line 1:
	{{distinguish\|Computational biology}}		{{distinguish\|Computational biology}}
	'''Bio-inspired computing''', short for '''biologically inspired computing''', is a field of study that loosely knits together subfields related to the topics of ], ] and ]. It is often closely related to the field of ], as many of its pursuits can be linked to ]. It relies heavily on the fields of ], ] and ]. Briefly put, it is the use of computers to model the living phenomena, and simultaneously the study of life to improve the usage of computers. Biologically inspired computing is a major subset of natural computation.		'''Bio-inspired computing''', short for '''biologically inspired computing''', is a field of study that loosely knits together subfields related to the topics of ], ] and ]. It is often closely related to the field of ], as many of its pursuits can be linked to ]. It relies heavily on the fields of ], ] and ]. Briefly put, it is the use of computers to model the living phenomena, and simultaneously the study of life to improve the usage of computers. Biologically inspired computing is a major subset of ].

	== Areas of research ==		== Areas of research ==

Revision as of 08:50, 11 February 2013

Not to be confused with Computational biology.

Bio-inspired computing, short for biologically 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 the living phenomena, and simultaneously the study of life 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 wave", heart conditions, axons, etc.
sensor networks ↔ sensory organs

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). For this reason, in neural network models, it is necessary to accurately model an in vivo network, by live collection of "noise" coefficients that can be used to refine statistical inference and extrapolation as system complexity increases.

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 millions of years have produced remarkably complex organisms. A similar technique is used in genetic algorithms.

References

http://www.duke.edu/~jme17/Joshua_E._Mendoza-Elias/Research_Interests.html#Neuroscience_-_Neural_Plasticity_in

External links

Categories:

Misplaced Pages