Using spreadsheets as learning tools for neural network simulation




computer simulation, neural networks, spreadsheets, neural computing, early network models, Anderson's Iris, cloud-based learning tools


The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using 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 On the article, methods for creating neural network models in Google Sheets, a cloud-based spreadsheet, are discussed. The classification of multidimensional data presented in R. A. Fisher's "The Use of Multiple Measurements in Taxonomic Problems" served as the model's primary inspiration. Discussed are various idiosyncrasies of data selection as well as Edgar Anderson's participation in the 1920s and 1930s data preparation and collection. The approach of multi-dimensional data display in the form of an ideograph, created by Anderson and regarded as one of the first effective methods of data visualization, is discussed here.


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Abelson, H., Sussman, G. J., & Sussman, J. (1996). Structure and Interpretation of Computer Programs (2nd ed.). Cambridge: MIT Press.

Abraham, T. H. (2002). (Physio)logical circuits: The intellectual origins of the McCulloch-Pitts neural networks. Journal of the History of the Behavioral Sciences, 38(1), 3–25. DOI: DOI:

Anderson, E. (1928). The Problem of Species in the Northern Blue Flags, Iris versicolor L. and Iris virginica L. Annals of the Missouri Botanical Garden, 15(3), 241–332. DOI: DOI:

Anderson, E. (1935). The Irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

Anderson, E. (1936). The Species Problem in Iris. Annals of the Missouri Botanical Garden, 23(3), 457–469+471–483+485–501+503–509. DOI: DOI:

Anderson, E. (1952). Plants, Man and Life. Boston: University of California Press. DOI:

Ayed, A. S. (1997). Master thesis. Memorial University.

Buergermeister, J. J. (1990). Using Computer Spreadsheets for Instruction in Cost Control Curriculum at the Undergraduate Level. In D. W. Dalton (Ed.), Proceedings of the 32nd Annual International Conference of the Association for the Development of Computer-Based Instructional Systems, San Diego, California, October 29-November 1, 1990 (pp. 214–220). Columbus: ADCIS International.

Chernoff, H. (1973). The Use of Faces to Represent Points in k-Dimensional Space Graphically. Journal of the American Statistical Association, 68(342), 361-368. DOI: DOI:

Cowan, J. D. (1998). Interview with J. A. Anderson and E. Rosenfeld. In J. A. Anderson & E. Rosenfeld (Eds.), Talking nets: An oral history of neural networks (pp. 97–124). Cambridge: MIT Press.

Cull, P. (2007). The mathematical biophysics of Nicolas Rashevsky. BioSystems, 88(3), 178–184. DOI: DOI:

Eberhart, R. C. & Dobbins, R. W. (1990). Background and History. In R.C. Eberhart & R. W. Dobbins (Eds.), Neural Network PC Tools: A Practical Guide (pp. 9–34). San Diego: Academic Press. DOI: DOI:

Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI: DOI:

Freedman, R. S., Frail, R. P., Schneider, F. T., & Schnitta, B. (1991). Expert systems in spreadsheets: modeling the Wall Street user domain. In Proceedings First International Conference on Artificial Intelligence Applications on Wall Street (pp. 296-301). DOI: DOI:

Hegazy, T. & Ayed, A. (1998). Neural Network Model for Parametric Cost Estimation of Highway Projects. Journal of Construction Engineering and Management, 124(3), 210–218. DOI: DOI:

Hewett, T. T. (1985a). Teaching Students to Model Neural Circuits and Neural Networks Using an Electronic Spreadsheet Simulator. Behavior Research Methods, Instruments, & Computers, 17(2), 339–344. DOI: DOI:

Hewett, T. T. (1985b). Using an Electronic Spreadsheet Simulator to Teach Neural Modeling of Visual Phenomena. Philadelphia: Drexel University.

Householder, A. S. (1940). A neural mechanism for discrimination: II. Discrimination of weights. Bulletin of Mathematical Biophysics, 2(1), 1–13. DOI: DOI:

Householder, A. S. (1941). A theory of steady-state activity in nerve-fiber networks I: Definitions and Preliminary Lemmas. Bulletin of Mathematical Biophysics, 3(2), 63–69. DOI: DOI:

Householder, A. S. & Landahl, H. D. (1945). Mathematical Biophysics of the Central Nervous System. Bloomington: Principia Press.

James, W. (1890). The Principles of Psychology. New York: Henry Holt and Company. DOI:

James, W. (1892). Psychology. New York: Henry Holt and Company.

Johnston, S. J. (1991). InfoWorld, 13(7), 14. DOI:

Kendrick, D. A., Mercado, P. R., & Amman, H. M. (2006). Computational Economics. Princeton: Princeton University Press. DOI:

Landahl, H. D. (1947). A matrix calculus for neural nets: II. Bulletin of Mathematical Biophysics, 9(2), 99–108. DOI: DOI:

Landahl, H. D., McCulloch, W. S., & Pitts, W. (1943). A statistical consequence of the logical calculus of nervous nets. Bulletin of Mathematical Biophysics, 5(4), 135–137. DOI: DOI:

Landahl, H. D. & Runge, R. (1946). Outline of a matrix calculus for neural nets. Bulletin of Mathematical Biophysics, 8(2), 75–81. DOI: DOI:

Markova, O., Semerikov, S., & Popel, M. (2018). CoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. CEUR Workshop Proceedings, 2104, 204. Retrieved from DOI:

Markova, O. M., Semerikov, S. O., Striuk, A. M., Shalatska, H. M., Nechypurenko, P. P., & Tron, V. V. (2019). Implementation of cloud service models in training of future information technology specialists. CEUR Workshop Proceedings, 2433, 499-515. Retrieved from DOI:

McCulloch, W. C. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133. DOI: DOI:

Mitchell, T. M. (2017). Key Ideas in Machine Learning. Retrieved from

Permiakova, O. S. & Semerikov, S. O. (2008). Zastosuvannia neironnykh merezh u zadachakh prohnozuvannia (The use of neural networks in forecasting problems). In Materials of the International Scientific and Practical Conference “Young scientist of the XXI century”, KTU, Kryviy Rih, 17–18 November 2008.

Pitts, W. (1942a). Some observations on the simple neuron circuit. Bulletin of Mathematical Biophysics, 4(3), 121–129. DOI: DOI:

Pitts, W. (1942b). The linear theory of neuron networks: The static problem. Bulletin of Mathematical Biophysics, 4(4), 169–175. DOI: DOI:

Pitts, W. (1943a). A general theory of learning and conditioning: Part I. Psychometrika, 8(1), 1–18. DOI: DOI:

Pitts, W. (1943b). A general theory of learning and conditioning: Part II. Psychometrika, 8(2), 131–140. DOI: DOI:

Pitts, W. (1943c). The linear theory of neuron networks: The dynamic problem. Bulletin of Mathematical Biophysics, 5(1), 23–31. DOI: DOI:

Pitts, W. & McCulloch, W. S. (1947). How we know universals the perception of auditory and visual forms. Bulletin of Mathematical Biophysics, 9(3), 127–147. DOI: DOI:

Rashevsky, N. (1933). Outline of a physico-mathematical theory of excitation and inhibition. Protoplasma, 20(1), 42–56. DOI: DOI:

Rashevsky, N. (1945a). Mathematical biophysics of abstraction and logical thinking. Bulletin of Mathematical Biophysics, 7(3), 133–148. DOI: DOI:

Rashevsky, N. (1945b). Some remarks on the boolean algebra of nervous nets in mathematical biophysics. Bulletin of Mathematical Biophysics, 7(4), 203–211. DOI: DOI:

Rashevsky, N. (1946). The neural mechanism of logical thinking. Bulletin of Mathematical Biophysics, 8(1), 29–40. DOI: DOI:

Rienzo, T. F. & Athappilly, K. K. (2012). Introducing Artificial Neural Networks through a Spread-sheet Model. Decision Sciences Journal of Innovative Education, 10(4), 515–520. DOI: DOI:

Ruggiero, M. (1993). U.S. Patent No. 5,241,620.

Ruggiero, M. A. (1997). Cybernetic Trading Strategies: Developing a Profitable Trading System with State-of-the-Art Technologies. New York: John Wiley & Sons.

Schwab, K. & Davis, N. (2018). Shaping the Fourth Industrial Revolution. London: Portfolio Penguin.

Semerikov, S. O. & Teplytskyi, I. O. (2018). Metodyka uvedennia osnov Machine learning u shkilnomu kursi informatyky (Methods of introducing the basics of Machine learning in the school course of informatics). In Problems of informatization of the educational process in institutions of general secondary and higher education. Ukrainian scientific and practical conference, Kyiv, October 09, 2018 (pp. 18–20). Kyiv: Vyd-vo NPU imeni M. P. Drahomanova.

Semerikov, S. O., Teplytskyi, I. O., Yechkalo, Yu. V., & Kiv, A. E. (2018). Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. CEUR Workshop Proceedings, 2257, 14. Retrieved from DOI:

Shimbel, A. & Rapoport, A. (1948). A statistical approach to the theory of the central nervous system. Bulletin of Mathematical Biophysics, 10(2), 41–55. DOI: DOI:

Stebbins, G. L. (1978). Edgar Anderson 1897-1969. Washington: National Academy of Sciences.

Sussman, G. J. & Wisdom, J. (2015). Structure and interpretation of classical mechanics (2nd ed.). Cambridge: MIT Press.

Teplytskyi, I. O. (2010). Elementy kompiuternoho modeliuvannia (Elements of computer simulation) (2nd ed.). Kryvyi Rih: KSPU.

Teplytskyi, I. O., Teplytskyi, O. I., & Humeniuk, A. P. (2008). Simulation environments: from replacement to integration. New computer technology, 6, 67–68.

Wei, T. (1948). On matrices of neural nets. Bulletin of Mathematical Biophysics, 10(2), 63–67. DOI: DOI:

Werbos, P. J. (1989). Maximizing long-term gas industry profits in two minutes in Lotus using neural network methods. Transactions on Systems Man and Cybernetics, 19(2), 315–333. DOI: DOI:

Young, G. (1941). On reinforcement and interference between stimuli. Bulletin of Mathematical Biophysics, 3(1), 5–12. DOI: DOI:

Zaremba, T. (1990). Case Study III: Technology in Search of a Buck. In R.C. Eberhart & R. W. Dobbins (Eds.), Neural Network PC Tools: A Practical Guide (pp. 251–283). San Diego: Academic Press. DOI: DOI:





How to Cite

Semerikov, S., Teplytskyi, I., Yechkalo, Y., Markova, O., Soloviev, V., & Kiv, A. (2022). Using spreadsheets as learning tools for neural network simulation. Ukrainian Journal of Educational Studies and Information Technology, 10(3), 42–68.