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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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Related Experiment Video

Updated: May 8, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Modeling brain function: current developments and future prospects.

Daniel Rasmussen, Chris Eliasmith

    JAMA Neurology
    |August 28, 2013
    PubMed
    Summary
    This summary is machine-generated.

    Large-scale brain simulations using millions of neurons are advancing our understanding of brain function. While not yet whole-brain models, these simulations connect diverse data, paving the way for future progress.

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    Area of Science:

    • Computational neuroscience
    • Systems neuroscience

    Background:

    • Functional whole-brain models are crucial for understanding complex neural processes.
    • Current models are limited in scale and scope, hindering comprehensive brain analysis.

    Purpose of the Study:

    • To review progress in developing large-scale neural simulations for whole-brain modeling.
    • To highlight the potential of these models in integrating diverse neuroscientific data.
    • To identify future research directions for achieving comprehensive whole-brain models.

    Main Methods:

    • Implementation of large-scale simulations involving millions of individual neurons.
    • Analysis of model capabilities in explaining behavioral, neurophysiological, and neuroanatomical data.

    Main Results:

    • Demonstrated ability of current models to explain various types of neuroscientific data.
    • Identification of the gap between current models and the goal of whole-brain simulation.

    Conclusions:

    • Large-scale neural simulations show promise for advancing brain understanding by linking different analytical levels.
    • Continued research is necessary to overcome current limitations and achieve functional whole-brain models.