(often hyphenated as 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
. 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
. 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:
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.
(the following are presented in ascending order of complexity and depth, with those new to the field suggested to start from the top)
- " Get A-life"
- " Digital Biology", Peter J. Bentley.
- " First International Symposium on Biologically Inspired Computing"
- Emergence: The Connected Lives of Ants, Brains, Cities and Software, Steven Johnson.
- Dr. Dobb's Journal, Apr-1991. (Issue theme: Biocomputing)
- Turtles, Termites and Traffic Jams, Mitchel Resnick.
- Understanding Nonlinear Dynamics, Daniel Kaplan and Leon Glass.
- Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, L. N. de Castro, Chapman & Hall/CRC, June 2006.
- " The Computational Beauty of Nature", Gary William Flake MIT Press. 1998, hardcover ed.; 2000, paperback ed. An in-depth discussion of many of the topics and underlying themes of bio-inspired computing.
- Kevin M. Passino, Biomimicry for Optimization, Control, and Automation, Springer-Verlag, London, UK, 2005.
- Recent Developments in Biologically Inspired Computing, L. N. de Castro and F. J. Von Zuben, Idea Group Publishing, 2004.
- Nancy Forbes, Imitation of Life: How Biology is Inspiring Computing, MIT Press, Cambridge, MA 2004.
- " Biologically Inspired Computing Lecture Notes", Luis M. Rocha
- The portable UNIX programming system (PUPS) and CANTOR: a computational envorionment for dynamical representation and analysis of complex neurobiological data, Mark A O'Neill, and Claus-C Hilgetag, Phil Trans R Soc Lond B 356 (2001), 1259-1276