{"title":"Biological computation: hearts and flytraps","authors":"Kay L. Kirkpatrick","doi":"10.1007/s10867-021-09590-9","DOIUrl":null,"url":null,"abstract":"<div><p>The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.</p></div>","PeriodicalId":612,"journal":{"name":"Journal of Biological Physics","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10867-021-09590-9.pdf","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biological Physics","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10867-021-09590-9","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 2
Abstract
The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.
期刊介绍：
Many physicists are turning their attention to domains that were not traditionally part of physics and are applying the sophisticated tools of theoretical, computational and experimental physics to investigate biological processes, systems and materials.
The Journal of Biological Physics provides a medium where this growing community of scientists can publish its results and discuss its aims and methods. It welcomes papers which use the tools of physics in an innovative way to study biological problems, as well as research aimed at providing a better understanding of the physical principles underlying biological processes.
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