User:GreatContributor1/Bio-inspired computing

History (Revised Contribution)
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. Turing firstly described the abstract construct using a biological specimen. Turing imagined a mathematician that has three important attributes. 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.

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. They first mathematically described that a system of simplistic neurons was able to produce simple logical operations such as logical conjunction, 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 book by Marvin Minsky and Seymour Papert as the main cause. 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 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. 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. 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. Lastly Hölder and Wilson in 2009 concluded using historical data that ants have evolved to function as a single "superogranism" colony. A very important result since it suggested that group selection evolutionary algorithms coupled together with algorithms similar to the "ant colony" can be potentially used to develop more powerful algorithms.

History
Early Ideas

The ideas behind biological computing trace back to the Turing machine [hyperlink the Turing machine] first described in 1936. Turing firstly described the abstract construct using a biological specimen.Turing imagined a mathematician that has three important attributes. 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.

[For this section, I would recommend starting off with the Turing machine first and expand on how he based it off biological systems. That way, it would flow better into the next section that talks more specifically about Neural Networks. What biological specimens did Turing base his machine off of?]

Neural Networks

Neural networks were first described by McCulloch and Pitts in 1943 [Include the full names of the people mentioned that way it is easier for people to research them]. They are one of the most famous examples of computational algorithms being inspired by biological systems [Reword to: Neural networks are a prevalent example of biological systems inspiring the creation of computer algorithms.] [Include specific sources that clearly point out the prevalence of the algorithm]. They first mathematically described that a system of simplistic neurons together was able to produce some simple logical operations [Include brief examples]. They first mathematically described that a system of simplistic neurons together.

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 book by Minsky and Papert as the main cause [Include full name of people, also hyperlink the book if there's a Wiki page on it][Also add a source where it claims that the book is responsible for the slowing down of neural network research]. Their book showed that neural network models were able to calculate only linear threshold functions, showing that a large amount of systems cannot be represented as such.[Be more clear about what the systems cannot be represented as] Another book by Rumelhart et al. in 1986 brought neural networks back to the spotlight. demonstrating the linear back-propagation algorithm something that allowed the development of multi-layered neural networks that did not have those limits. [ Reword to: The book demonstrated that the linear back propagation algorithm did not have those limits as it allowed for the development of multi layered neural networks].

[For this section, I think it'd be helpful to define more clearly what neural networks are, or hyperlink a Wiki page for it, if it exists.]

Other algorithms in nature.

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. 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." [Reword to: More specifically, he used an ant colony as an example. As a colony, ants can carry out intelligent tasks that the individual ant cannot do. This is referred to as "emergent behavior." -It also may be helpful to hyperlink or cite the specific source used]

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. Lastly Hölder and Wilson in 2009 concluded using historical data that ants have evolved to function as a single "superogranism" colony. [cite or hyperlink the specific study] A very important result since it suggested that group selection "evolutionary algorithms" coupled together with algorithms similar to the "ant colony" can be used to develop more powerful algorithms.

Notes: HI! I think this a great start! The main thing that I would suggest is to add more background info in the places where I indicated. Good luck!