Search

Brain research has in recent years indisputably entered a new epoch, driven bysubstantialmethodologicaladvances and digitallyenabled data integration and modeling at multiple scales –from molecules to the whole system. Major advances are emerging at theintersection of neurosciencewith technology and computing. This new science of the brain integrates high-quality basic research,systematic data integration across multiple scales, a new culture of large-scale collaboration andtranslation into applications. A systematic approach, as pioneered in Europe’s Human Brain Project(HBP), will be essential in meeting the pressing medical andtechnological challenges of the comingdecade. The aims of this paper are To develop a concept for the coming decade of digital brain research To discuss it with the research community at large, with the aim of identifying points of convergence and common goals To provide a scientific framework for current and future development of EBRAINS To inform and engage stakeholders, funding organizations and research institutions regarding future digital brain research To identify and address key ethical and societal issues While we do not claim that there is a ‘one size fits all’ approach to addressing these aspects, we areconvinced that discussions around the theme of digital brain research will help drive progress in thebroader field of neuroscience. You can submit your comments here and be considered for co-authorship Or become a supporter by clicking here More information below Comments on this manuscript are welcome This manuscriptis a living document that is being further developed in a participatory process. The work has been initiated by the Science and Infrastructure Board of the Human Brain Project (HBP). Now, the entire research community is invited to contribute to shaping the vision by submitting comments.Comments can be submitted via an online commentary form here. All submitted comments will be considered and discussed. The final decision on whether edits or additions will be made to the next version of the manuscript based on an individual comment will be made by the Science and Infrastructure Board (SIB) of the Human Brain Project (HBP) at regular intervals. The final version of the manuscript will be prepared and published in the next months. Comments may be submitted until the end of May, 2023. Every researcher is welcome to provide comment or indicate support for the contents of this paper and jointly shape the future of brain research. Supporters of the paper: Pietro Avanzini, Marc Beyer, Maria Del Vecchio, Jitka Annen, Maurizio Mattia, Steven Laureys, Rosanne Edelenbosch, Rafael Yuste, Jean-Pierre Changeux, Linda Richards, Hye Weon Jessica Kim, Chrysoula Samara, Luis Miguel González de la Garza, Nikoleta Petalidou, Vasudha Kulkarni, Cesar David Rincon, Isabella O'Shea, Munira Tamim Electricwala, Nicola Palomero-Gallagher, Bernd Carsten Stahl, Bahar Hazal Yalcinkaya, Meysam Hashemi, Carola Sales Carbonell, Marcel Carrère, Anthony Randal McIntosh, Hiba Sheheitli, Abolfazl Ziaeemehr, Martin Breyton, Giovanna Ramos Queda, Anirudh NIhalani Vattikonda, Gyorgy Buzsaki George Ogoh, William Knight Torbjørn V Ness, MIchiel van der Vlag, Mu-ming Poo.

Brain research has in recent years indisputably entered a new epoch, driven bysubstantialmethodologicaladvances and digitallyenabled data integration and modeling at multiple scales –from molecules to the whole system. Major advances are emerging at theintersection of neurosciencewith technology and computing. This new science of the brain integrates high-quality basic research,systematic data integration across multiple scales, a new culture of large-scale collaboration andtranslation into applications. A systematic approach, as pioneered in Europe’s Human Brain Project(HBP), will be essential in meeting the pressing medical andtechnological challenges of the comingdecade. The aims of this paper are To develop a concept for the coming decade of digital brain research To discuss it with the research community at large, with the aim of identifying points of convergence and common goals To provide a scientific framework for current and future development of EBRAINS To inform and engage stakeholders, funding organizations and research institutions regarding future digital brain research To identify and address key ethical and societal issues While we do not claim that there is a ‘one size fits all’ approach to addressing these aspects, we areconvinced that discussions around the theme of digital brain research will help drive progress in thebroader field of neuroscience. You can submit your comments here and be considered for co-authorship Or become a supporter by clicking here More information below Comments on this manuscript are welcome This manuscriptis a living document that is being further developed in a participatory process. The work has been initiated by the Science and Infrastructure Board of the Human Brain Project (HBP). Now, the entire research community is invited to contribute to shaping the vision by submitting comments.Comments can be submitted via an online commentary form here. All submitted comments will be considered and discussed. The final decision on whether edits or additions will be made to the next version of the manuscript based on an individual comment will be made by the Science and Infrastructure Board (SIB) of the Human Brain Project (HBP) at regular intervals. New versions of the manuscript will be published every few months on Zenodo. Comments may be submitted until the beginning of 2023. During the Human Brain Project Summit 2023, the manuscript will be adopted by HBP and non-HBP participants, and a final version will be published shortly after. Supporters of the paper: Pietro Avanzini, Marc Beyer, Maria Del Vecchio, Jitka Annen, Maurizio Mattia, Steven Laureys, Rosanne Edelenbosch, Rafael Yuste, Jean-Pierre Changeux, Linda Richards.

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.

Human behaviour is dependent on the ability of neuronal circuits to predict the outside world. Neuronal circuits make these predictions based on internal models. Despite our extensive knowledge of the sensory features that drive cortical neurons, we have a limited grasp on the structure of the brain's internal models. Substantial progress in neuroscience therefore depends on our ability to replicate the models that the brain creates internally. Here we record human fMRI data while presenting partially occluded visual scenes. Visual occlusion controls sensory input to subregions of visual cortex while internal models continue to influence activity in these regions. Since the observed activity is dependent on internal models, but not on sensory input, we have the opportunity to map the features of the brain's internal models. Our results show that internal models in early visual cortex are both categorical and scene-specific. We further demonstrate that behavioural line drawings provide a good description of internal model structure. These findings extend our understanding of internal models by showing that line drawings, which have been effectively used by humans to convey information about the world for thousands of years, provide a window into our brains' internal models of vision.

Early visual cortical neurons receive highly selective feedforward input, which is amplified or disamplified by contextual feedback and lateral connections. A significant challenge for systems neuroscience is to measure the feature space that drives these feedback channels. We occluded visual scenes and measured non-feedforward stimulated subregions of V1 and V2 using fMRI and multi-voxel pattern analyses. We found that response patterns in these subregions contain two high-level scene features, category and depth information. Responses in non-feedforward stimulated V1 and V2 differed from each other, suggesting that feedback to these two areas has unique information content. Further, we reveal that computational models of visual processing inadequately describe early visual cortex because they do not account for the brain's internal modelling of the world.