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For several decades, computing science and neuroscience benefit from fruitful synergy. This special issue searches to make a step forward by providing an overview of how the new advancements in computing sciences strengthen and enrich this synergy. The general aim of this special issue, continuing the neurocybernetic concepts from Wiener and W.S. McCulloch, is to present a wider and more comprehensive relation of the computational paradigm (CP), with emerging neuroscience studies. The secondary objectives are also (1) to help neuroscience and cognitive science, by explaining the latter as a result of the former, (2) to establish an interaction framework between neural computation and neuroscience by posing a series of appropriate questions in both directions of the interaction, from artificial systems to neural systems, and from neural systems to artificial systems. Nowadays, machine learning holds great promise in the development of new models and theories in the field of neuroscience, in conjunction with classical statistical hypothesis testing. Machine learning algorithms have the potential to reveal interactions, hidden patterns of abnormal activity, brain structure and connectivity and physiological mechanisms of brain and behavior. In addition, several approaches for testing the significance of the machine learning outcomes have been successfully proposed to avoid ‘‘the dangers of spurious findings or explanations void of mechanism’’ utilizing proper replication, validation and hypothesis-driven confirmation. Therefore, these new trends in machine learning can effectively provide relevant information to take great strides toward understanding how the brain works. The main goal of this field is to build new and redesign old bridges between the two scientific communities, the artificial intelligence community, including deep learning and related applications within pattern recognition, and the neuroscience community. Deep learning has meant a breakthrough in the artificial intelligence community. The best performances attained so far in many fields, such as computer vision or natural language processing, have been overtaken by these novel paradigms up to a point that only 10 years ago was just science fiction. In addition, this technology has been opensourced by the main AI companies, hence making quite straightforward to design, train and integrate deep-learning-based systems. Moreover, the amount of data available every day is not only enormous, but growing at an exponential rate. Over the last years, there has been an increasing interest in using machine learning methods to analyze and visualize massive data generated from very different sources and with many different features: social networks, surveillance systems, smart cities, medical diagnosis, business, cyberphysical systems or media digital data. The design of neural systems based on neuroscience with high computing requirements evidence opens a huge opportunity for new applications. The International Work conference on the Interplay between Natural and Artificial Computation (IWINAC) meeting brought successfully together researchers in neurobiology, computational neuroscience and artificial intelligence. After the meeting, research involving neuroscience and computation grows together with the novel developments of both disciplines: Neuroscience is the branch of science when the discoveries of the century take place, and computing elements are becoming omnipresent, cheaper and more skillful, often thanks to interdisciplinary approaches. The positive outcome of that experience encouraged us in further exploration on the intersection of these disciplines in the hope to find new paradigms and techniques. This Neural Computing and Applications special issue covers the extended and updated versions of a set of the works presented at IWINAC conference. & J. M. Ferrández [email protected]