Misplaced Pages

Bio-inspired computing

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.

This is an old revision of this page, as edited by Jarble (talk | contribs) at 03:38, 26 November 2017 (adding links using Google Scholar). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Revision as of 03:38, 26 November 2017 by Jarble (talk | contribs) (adding links using Google Scholar)(diff) ← Previous revision | Latest revision (diff) | Newer revision → (diff)
This article may require cleanup to meet Misplaced Pages's quality standards. The specific problem is: This article has potential, but is currently mostly used as a coatrack for WP:REFSPAM. Please help improve this article if you can. (August 2016) (Learn how and when to remove this message)
It has been suggested that this article be merged with Biological computing. (Discuss) Proposed since December 2016.
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:

Artificial intelligence

The way in which bio-inspired computing differs from the traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to 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. For example, training a virtual insect to navigate in an unknown terrain for finding food includes six simple rules. The insect is trained to

  • turn right for target-and-obstacle left;
  • turn left for target-and-obstacle right;
  • turn left for target-left-obstacle-right;
  • turn right for target-right-obstacle-left,
  • turn left for target-left without obstacle and
  • turn right for target right without obstacle.

The virtual insect controlled by the trained spiking neural network can find food after training in any unknown terrain. 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.

See also

This article is in list format but may read better as prose. You can help by converting this article, if appropriate. Editing help is available. (December 2016)
Lists

References

  1. Xu Z; Ziye X; Craig H; Silvia F (Dec 2013). "Spike-based indirect training of a spiking neural network-controlled virtual insect". Decision and Control (CDC), IEEE: 6798–6805. doi:10.1109/CDC.2013.6760966. ISBN 978-1-4673-5717-3.
  2. Joshua E. Mendoza. ""Smart Vaccines" - The Shape of Things to Come". Research Interests. Archived from the original on November 14, 2012.

Further reading

(the following are presented in ascending order of complexity and depth, with those new to the field suggested to start from the top)

External links

Categories: