The model of artificial neural nets (SSNs) is based on analogies to biological counterparts, such as a simplified model of neuron and retinal neurons. Due to increasing complexity and problems with the development of effective methods of deep learning of SSNs, artificial neural networks dominated solutions based on algebraic structures. Currently advanced approaches in machine learning such as deep learning exhibit a number of undesirable features such as forgetting, vulnerability to fraudulent examples, requiring a large training set, and slow learning. None of these features occur in the context of brain biological activity, suggesting that it would be beneficial to return to a stronger inspiration of SSNs with the help of biological neuronal systems. The aim of the project is to analyse the high-level behaviour of neuronal systems and to build innovative SSN models by proposing new learning paradigms and new architectures of computational models