Neuroinformatics is the field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied: the development of computational models of the nervous system and neural processes. the development of tools for analyzing and modeling neuroscience data, the development of tools and databases for management and sharing of neuroscience data at all levels of analysis, Neuroinformatics is related with philosophy (computational theory of mind), psychology (information processing theory), computer science (natural computing, bio-inspired computing), among others. Neuroinformatics doesn't deal with matter or energy, so it can be seen as a branch of neurobiology that studies various aspects of nervous systems. The term neuroinformatics seems to be used synonymously with cognitive informatics, described by Journal of Biomedical Informatics as interdisciplinary domain that focuses on human information processing, mechanisms and processes within the context of computing and computing applications. According to German National Library, neuroinformatics is synonymous with neurocomputing. At Proceedings of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing was introduced the following description: Cognitive Informatics (CI) as a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science. CI investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. According to INCF, neuroinformatics is a research field devoted to the development of neuroscience data and knowledge bases together with computational models. Neuroinformatics in neuropsychology and neurobiology Models of neural computation Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. Due to the complexity of nervous system behavior, the associated experimental error bounds are ill-defined, but the relative merit of the different models of a particular subsystem can be compared according to how closely they reproduce real-world behaviors or respond to specific input signals. In the closely related field of computational neuroethology, the practice is to include the environment in the model in such a way that the loop is closed. In the cases where competing models are unavailable, or where only gross responses have been measured or quantified, a clearly formulated model can guide the scientist in designing experiments to probe biochemical mechanisms or network connectivity. Neurocomputing technologies Artificial neural networks Artificial neural