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The role of neural network modeling in the learning content of the specialcourse "Foundations of Mathematical Informatics" was discussed. The course wasdeveloped for the students of technical universities - future IT-specialistsand directed to breaking the gap between theoretic computer science and it'sapplied applications: software, system and computing engineering. CoCalc wasjustified as a learning tool of mathematical informatics in general and neuralnetwork modeling in particular. The elements of technique of using CoCalc atstudying topic "Neural network and pattern recognition" of the special course"Foundations of Mathematic Informatics" are shown. The program code waspresented in a CoffeeScript language, which implements the basic components ofartificial neural network: neurons, synaptic connections, functions ofactivations (tangential, sigmoid, stepped) and their derivatives, methods ofcalculating the network's weights, etc. The features of the Kolmogorov-Arnoldrepresentation theorem application were discussed for determination thearchitecture of multilayer neural networks. The implementation of thedisjunctive logical element and approximation of an arbitrary function using athree-layer neural network were given as an examples. According to thesimulation results, a conclusion was made as for the limits of the use ofconstructed networks, in which they retain their adequacy. The framework topicsof individual research of the artificial neural networks is proposed.

CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"