Biologically inspired computing

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最新推荐文章于 2024-06-23 13:09:18 发布

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分类专栏：生物信息学文章标签： optimization network protocols system networking generation

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本文介绍了生物启发计算这一领域，它通过模拟自然界中的现象来解决计算问题。该领域涉及遗传算法、细胞自动机、人工生命等多个方面，并探讨了其与人工智能的关系。

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That's when people see bats they designed the RADAR. We learn from nature.

From Wikipedia: http://en.wikipedia.org/wiki/Biologically_inspired_computing

Biologically inspired computing

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Not to be confused with Computational biology.

Biologically inspired (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 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.

[hide]

1 Areas of research
2 Bio-inspired computing and AI
3 See also
4 References
5 Further reading
6 External links

[edit] 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

[edit] 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 ^[1].

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.

[edit] See also

[edit] References

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

[edit] 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)

"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
Going Back to our Roots: Second Generation Biocomputing, J. Timmis, M. Amos, W. Banzhaf, and A. Tyrrell, Journal of Unconventional Computing 2 (2007) 349 - 378.