Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot
Abstract
The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of thirdparty addins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet addins for nonlinear optimization, creation of neural networks in the spreadsheet environment without addins and macros. After analyzing a collection of writings of 18901950, the research determines the role of the scientific journal "Bulletin of Mathematical Biophysics", its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods  the continuous twofactor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discretecontinuous models of Householder and Landahl.
 Publication:

arXiv eprints
 Pub Date:
 June 2018
 DOI:
 10.48550/arXiv.1807.00018
 arXiv:
 arXiv:1807.00018
 Bibcode:
 2018arXiv180700018S
 Keywords:

 Computer Science  Computers and Society;
 68T99;
 K.3.1;
 I.2.6;
 K.2
 EPrint:
 26 pages, 8 figures