Research Tools and Paradigms

  • Jaime Gómez-Ramirez
Chapter
First Online:
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 7)

Abstract

Biology in the twenty-first century will be for Mathematics what Physics was in nineteenth and twentieth centuries. This is a well accepted belief among scientists with mathematical training. Some physicists can go even further and deplore the pre-Newtonian stage in which biology and in particular neuroscience find themselves (Mazzocchi F, EMBO Rep 9(1):10–14, 2008). Neuroscience is a data-rich field that needs for a theoretical framework that guides the model building and simulation processes. Biology, in opposition to physics which is quantitative and explanatory, may be perceived as a descriptive and qualitative field. However, to state that biology is descriptive and qualitative, while physics is mathematical and quantitative is a dichotomy too simplistic to be true (as all dichotomies usually are). Undoubtedly, biology today is quantitative in either its models and results. It is easily noticeable that in top scientific journals such as Nature or Science, though they are not devoted to any particular field, the studies in the field of biology, specifically molecular biology, are the overwhelming majority of the total of articles. The very dissimilar rate of production that these two prestigious publications display between disciplines other than biology is such, that one starts wondering if the non-life science community has anything left important to say. But the actual reason for this must be found in the scarcity of mathematized universal principles in biology compared to, for example, physics. Physics is built on formal theories embedded in universal laws, rendering particulars and details unnecessary. In biology, on the other hand, particular cases are relevant. Furthermore, variability, non linearity, noise or high dimensionality are biological features hardly generalizable in the standard mathematical forms (Gómez-Ramirez J, Sanz R, Prog Biophys Mol Biol 2013).

Keywords

Large Hadron Collider Category Theory Brain Science Associative Neural Network Biology Today 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Jaime Gómez-Ramirez
    • 1
  1. 1.Departamento de AutomáticaUniversidad Politécnica de MadridMadridSpain

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