  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 third-party add-ins to spreadsheets, development of macros using the
embedded languages of spreadsheets; use of standard spreadsheet add-ins for
non-linear optimization, creation of neural networks in the spreadsheet
environment without add-ins and macros. After analyzing a collection of
writings of 1890-1950, 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 two-factor model of Rashevsky, the
discrete model of McCulloch and Pitts, and the discrete-continuous models of
Householder and Landahl.
