Editing Bio-inspired computing (section)

== History ==
'''Early Ideas'''

The ideas behind biological computing trace back to 1936 and the first description of an abstract computer, which is now known as a [[Turing machine]]. [[Alan Turing|Turing]] firstly described the abstract construct using a biological specimen. Turing imagined a mathematician that has three important attributes.<ref>{{Cite book |last=Turing |first=Alan |url=http://worldcat.org/oclc/18386775 |title=On computable numbers : with an application to the Entscheidungsproblem |date=1936 |publisher=Mathematical Society |oclc=18386775}}</ref> He always has a pencil with an eraser, an unlimited number of papers and a working set of eyes. The eyes allow the mathematician to see and perceive any symbols written on the paper while the pencil allows him to write and erase any symbols that he wants. Lastly, the unlimited paper allows him to store anything he wants memory. Using these ideas he was able to describe an abstraction of the modern digital computer. However Turing mentioned that anything that can perform these functions can be considered such a machine and he even said that even electricity should not be required to describe digital computation and machine thinking in general.<ref>{{Citation |last=Turing |first=Alan |title=Computing Machinery and Intelligence (1950) |date=2004-09-09 |url=http://dx.doi.org/10.1093/oso/9780198250791.003.0017 |work=The Essential Turing |pages=433–464 |publisher=Oxford University Press |doi=10.1093/oso/9780198250791.003.0017 |isbn=978-0-19-825079-1 |access-date=2022-05-05|url-access=subscription }}</ref>

'''Neural Networks'''

First described in 1943 by Warren McCulloch and Walter Pitts, neural networks are a prevalent example of biological systems inspiring the creation of computer algorithms.<ref>{{Citation |last1=McCulloch |first1=Warren |title=A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) |date=2021-02-02 |url=http://dx.doi.org/10.7551/mitpress/12274.003.0011 |work=Ideas That Created the Future |pages=79–88 |publisher=The MIT Press |access-date=2022-05-05 |last2=Pitts |first2=Walter|doi=10.7551/mitpress/12274.003.0011 |isbn=9780262363174 |s2cid=262231397 |url-access=subscription }}</ref> They first mathematically described that a system of simplistic neurons was able to produce simple [[logical operations]] such as [[logical conjunction]], [[Logical disjunction|disjunction]] and [[negation]]. They further showed that a system of neural networks can be used to carry out any calculation that requires finite memory. Around 1970 the research around neural networks slowed down and many consider a 1969 [[Perceptrons (book)|book]] by Marvin Minsky and Seymour Papert as the main cause.<ref>{{Cite book |last=Minsky |first=Marvin |url=http://worldcat.org/oclc/1047885158 |title=Perceptrons : an introduction to computational geometry |publisher=The MIT Press |year=1988 |isbn=978-0-262-34392-3 |oclc=1047885158}}</ref><ref>{{Cite web |title=History: The Past |url=https://userweb.ucs.louisiana.edu/~isb9112/dept/phil341/histconn.html |access-date=2022-05-05 |website=userweb.ucs.louisiana.edu}}</ref> Their book showed that neural network models were able only model systems that are based on Boolean functions that are true only after a certain threshold value. Such functions are also known as [[Linear classifier|threshold functions]]. The book also showed that a large amount of systems cannot be represented as such meaning that a large amount of systems cannot be modeled by neural networks. Another book by James Rumelhart and David McClelland in 1986 brought neural networks back to the spotlight by demonstrating the linear back-propagation algorithm something that allowed the development of multi-layered neural networks that did not adhere to those limits.<ref>{{Cite book |last1=McClelland |first1=James L. |last2=Rumelhart |first2=David E.|url=http://worldcat.org/oclc/916899323 |title=Parallel distributed processing : explorations in the microstructure of cognition. |date=1999 |publisher=MIT Press |isbn=0-262-18120-7 |oclc=916899323}}</ref>

'''Ant Colonies'''

Douglas Hofstadter in 1979 described an idea of a biological system capable of performing intelligent calculations even though the individuals comprising the system might not be intelligent.<ref>{{Cite book |last=Hofstadter |first=Douglas R. |url=http://worldcat.org/oclc/750541259 |title=Gödel, Escher, Bach : an eternal golden braid |date=1979 |publisher=Basic Books |isbn=0-465-02656-7 |oclc=750541259}}</ref> More specifically, he gave the example of an ant colony that can carry out intelligent tasks together but each individual ant cannot exhibiting something called "[[emergent behavior]]." Azimi et al. in 2009 showed that what they described as the "ant colony" algorithm, a clustering algorithm that is able to output the number of clusters and produce highly competitive final clusters comparable to other traditional algorithms.<ref>{{Citation |last1=Azimi |first1=Javad |title=Clustering Ensembles Using Ants Algorithm |date=2009 |url=http://dx.doi.org/10.1007/978-3-642-02264-7_31 |work=Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy |pages=295–304 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |isbn=978-3-642-02263-0 |access-date=2022-05-05 |last2=Cull |first2=Paul |last3=Fern |first3=Xiaoli|series=Lecture Notes in Computer Science |volume=5601 |doi=10.1007/978-3-642-02264-7_31 |url-access=subscription }}</ref> Lastly Hölder and Wilson in 2009 concluded using historical data that ants have evolved to function as a single "superogranism" colony.<ref>{{Cite journal |last1=Wilson |first1=David Sloan |last2=Sober |first2=Elliott |date=1989 |title=Reviving the superorganism |url=http://dx.doi.org/10.1016/s0022-5193(89)80169-9 |journal=Journal of Theoretical Biology |volume=136 |issue=3 |pages=337–356 |doi=10.1016/s0022-5193(89)80169-9 |pmid=2811397 |bibcode=1989JThBi.136..337W |issn=0022-5193|url-access=subscription }}</ref>  A very important result since it suggested that group selection [[evolutionary algorithm]]s coupled together with algorithms similar to the "ant colony" can be potentially used to develop more powerful algorithms.