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• Creative learning in computer science is necessary and important to define. • The variable creative learning in computing needs to be operationalized and measured through universal metrics and scales. • The development of creative learning in computer science does not depend entirely on the curriculum but on how it is taught. The training of creative computer professionals is considered a strategic process in different countries because it is essential for the computerization of society. This paper proposes an approach to the study of creative computer science learning for the digital transformation of modern society. The aim of the research was to evaluate the creative learning of computer science during the training of computer science professionals. An instrument derived from previous research and metrics was applied to evaluate creative learning in the two groups of terminal years of the computer engineering course at the University of Matanzas with a mixed paradigm. The quantitative analysis diagnosed that more than sixty-five percent of the students are evaluated as poor and fair in both years. The qualitative analysis revealed the inadequacies of the teaching staff in the use of computer science teaching approaches, and the four stages of formation of creative learning in computer science in the degree course: pre-university stage, basic stage, professional stage and research stage. They also revealed three key factors in the formation of creative learning in computer science: the integration of subjects, the solution of real projects, and the mainly emotional support provided by teachers and other professionals to students during their integration into the computerization of organizations. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]