The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

A Method for Recognizing and Describing the Links Among Dream Sources

A method is described for the identification of possible links among dream sources and the study of their possible significance. The analysis is based on the automatic recognition of word root recurrences in text files, including dream reports and associations. Two tools are then applied: graph representation and grammar analysis. Graph representation of the detected links provides a quantitative description of some of their basic features. Grammar changes for recurrent word roots can imply remarkable context changes. A plausible explanation of the identified context changes can evidence interesting phenomena connected to the significance of links among dream sources. Two examples of application of the method are given, 1 taken from the literature and the other from sleep lab data

Significance of automatically detected word recurrences in dream associations

Verbal data files including dream reports and associations with the report items were subjected to automatic analysis aiming at the recognition of word recurrences. The research was based on the following assumptions: the associations can provide information about the dream sources; the recognition of word recurrences in text files can be a useful tool for the study of dreaming; the identification of links between different dream sources can provide an interesting insight into the phenomenon of dreaming. The principal result obtained was that word recurrences often evidence possible significant links between dream sources. A number of the possible links evidenced by the automatic analysis not only escaped the subject's notice, but might also be unexpected for an analyzer not assisted by a computer