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Bringing Anatomical Information into Neuronal Network Models.

S J van Albada1,2, A Morales-Gregorio3,4,5, T Dickscheid6,7

  • 1Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience, Institute for Advanced Simulation (IAS-6) Theoretical Neuroscience, and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany. s.van.albada@fz-juelich.de.

Advances in Experimental Medicine and Biology
|April 26, 2022
PubMed
Summary
This summary is machine-generated.

Computational neuroscientists can now access guidance for building neuronal network models. This chapter details anatomical data types, experimental techniques, and predictive connectomics methods to fill data gaps for more accurate brain models.

Keywords:
Brain atlasesBrain connectivityCytoarchitectureNeuroanatomyPredictive connectomics

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Neuroanatomy

Background:

  • Constructing neuronal network models requires extensive anatomical data, often incomplete.
  • Integrating diverse data sources and estimating missing values presents significant challenges for researchers.

Purpose of the Study:

  • To guide computational neuroscientists in utilizing anatomical data for model construction.
  • To provide a framework for interpreting experimental techniques and data sets.
  • To introduce methods for addressing data gaps in neuronal modeling.

Main Methods:

  • Describing key anatomical data types relevant to neuronal network models.
  • Discussing experimental techniques and comprehensive data sets.
  • Explaining "predictive connectomics" for estimating missing connectivity data.

Main Results:

  • Identification of crucial anatomical data types and their interpretation.
  • Presentation of methods for "predictive connectomics" to fill experimental data gaps.
  • Highlighting organizational principles influencing neuronal network dynamics, particularly in the mammalian cerebral cortex.

Conclusions:

  • Systematic data collection and standardized representation in neuroanatomy are vital.
  • Predictive connectomics offers a powerful approach to overcome data limitations in computational neuroscience.
  • This work provides essential resources for building more comprehensive and accurate neuronal network models.