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Data-driven multiscale computational models of cortical and subcortical regions.

Srikanth Ramaswamy1

  • 1Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle Upon Tyne, NE2 4HH, United Kingdom.

Current Opinion in Neurobiology
|February 6, 2024
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Summary

Data-driven computational models are advancing brain research by integrating multiscale data from neurons to behavior. These models offer insights into brain function and dysfunction, driving neuroscience forward.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neuroscience

Background:

  • Data-driven computational models are crucial for understanding brain complexity.
  • These multiscale models integrate information across various biological scales.

Purpose of the Study:

  • To survey recent advances in data-driven computational models of mammalian neural networks.
  • To discuss challenges and opportunities in developing these models.

Main Methods:

  • Review of recent literature on data-driven computational models.
  • Discussion of interdisciplinary collaboration, model validation, and comparison.

Main Results:

  • Progress in creating data-driven models for cortical and subcortical areas.
  • Identification of key challenges including data integration and validation.

Conclusions:

  • Data-driven multiscale models are essential for advancing neuroscience.
  • Future directions involve interdisciplinary work and new technologies for predictive brain models.