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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Linking Brain Structure, Activity, and Cognitive Function through Computation.

Katrin Amunts1,2, Javier DeFelipe3,4, Cyriel Pennartz5

  • 1Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany k.amunts@fz-juelich.de.

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Summary
This summary is machine-generated.

Computational neuroscience, using advanced models and the BigBrain resource, aims to understand the human brain. This research bridges basic neuroscience and medicine, advancing neuro-inspired technologies and societal benefits.

Keywords:
artificial neuronal networksbrain complexityconnectivityhuman brain mappingmultiscale brain organizationneuro-inspired technology

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Area of Science:

  • Computational neuroscience
  • Neuro-inspired technologies
  • Digital research infrastructure

Background:

  • Understanding the human brain is a major 21st-century research challenge.
  • Computational approaches, including artificial neural networks, modeling, and simulation, are crucial for analyzing complex brain data.

Purpose of the Study:

  • To link brain structure and function using dynamic generative multiscale models.
  • To leverage resources like the BigBrain for integrated neuroscientific discovery.
  • To advance neuro-inspired technologies through interdisciplinary research.

Main Methods:

  • Utilizing dynamic generative multiscale models to investigate causation across scales.
  • Employing the BigBrain resource to spatially anchor tissue models and data.
  • Integrating neuroscience, computing, and robotics research.

Main Results:

  • Demonstrated the utility of multiscale models in linking brain structure and function.
  • Showcased the BigBrain as a key resource for data-driven neuroscientific discovery.
  • Highlighted the potential of integrating computational approaches with empirical data.

Conclusions:

  • Computational approaches and integrated resources like BigBrain are essential for understanding the human brain.
  • The Human Brain Project's EBRAINS infrastructure fosters collaboration for brain research.
  • Advancements in understanding the brain offer significant societal benefits and drive technological innovation.