Linking Brain Structure, Activity, and Cognitive Function through Computation

Katrin Amunts, Javier Defelipe, Cyriel Pennartz, Alain Destexhe, Michele Migliore, Philippe Ryvlin, Steve Furber, Alois Knoll, Lise Bitsch, Jan G. Bjaalie, Yannis Ioannidis, Thomas Lippert, Maria V. Sanchez-Vives, Rainer Goebel, Viktor Jirsa

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuro-science, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neu-roscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.

Original languageEnglish
Article numberENEURO.0316-21.2022
JournaleNeuro
Volume9
Issue number2
DOIs
StatePublished - 1 Mar 2022

Keywords

  • Artificial neuronal networks
  • Brain complexity
  • Connectivity
  • Human brain mapping
  • Multiscale brain organization
  • Neuro-inspired technology

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