Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neuron Structure

Neuron Structure

Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...

Neuron Structure

Neuron Structure

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cerebellar tonic inhibition orchestrates the maturation of information processing and motor coordination.

Experimental & molecular medicine·2026

Same author

The Ultrastructural Properties of the Endoplasmic Reticulum Govern Microdomain Signaling in Perisynaptic Astrocytic Processes.

Glia·2025

Same author

GABA-Induced Seizure-Like Events Caused by Multi-ionic Interactive Dynamics.

eNeuro·2024

Same author

Branch-specific clustered parallel fiber input controls dendritic computation in Purkinje cells.

iScience·2024

Same author

Vesicle and reaction-diffusion hybrid modeling with STEPS.

Communications biology·2024

Same author

Recent data on the cerebellum require new models and theories.

Current opinion in neurobiology·2023

Same journal

TC2-Res: a structured fusion of tract-level and connectome-level brain imaging in small-sample cohorts of athletes.

Frontiers in neuroanatomy·2026

Same journal

Revisiting the clastosome: a stress-induced nuclear proteolytic compartment of mammalian cells.

Frontiers in neuroanatomy·2026

Same journal

Intrinsic elaboration of prefrontal modularity: a dual-control model of axon bundling and synaptic docking.

Frontiers in neuroanatomy·2026

Same journal

Selective enrichment of TCF4 in GABAergic neurons during postnatal primate development.

Frontiers in neuroanatomy·2026

Same journal

ATF3-based peripheral neural tract tracing.

Frontiers in neuroanatomy·2026

Same journal

Stereological evaluation of the neuroprotective effects of curcumin on the spinal cord in a streptozotocin-induced diabetic rat model.

Frontiers in neuroanatomy·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Context-aware modeling of neuronal morphologies.

Benjamin Torben-Nielsen¹, Erik De Schutter²

¹Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University Onna son, Japan.

Frontiers in Neuroanatomy

|September 25, 2014

Summary

This summary is machine-generated.

Understanding neuronal morphology requires considering their brain environment. A new computational framework, NeuroMaC, simulates neuron growth within this context, accurately replicating diverse morphologies and their variations.

Keywords:

computational modeling dendrite extracellular space growth cone morphology

More Related Videos

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Apr 23, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Computational Neuroscience
Neurobiology
Developmental Neuroscience

Background:

Neuronal morphology is crucial for brain function, influencing circuit topology and signal integration.
Existing studies often neglect the developmental context, treating neurons in isolation.
The high diversity and variance in neuronal shapes suggest environmental interactions during development.

Purpose of the Study:

To propose and validate a context-aware computational framework for simulating neuronal morphology.
To investigate how environmental interactions influence neuronal shape and variation.
To generate accurate virtual neuronal morphologies for research and modeling.

Main Methods:

Development of NeuroMaC, a computational framework for simultaneous neuron growth.
Growth rules based on interactions between developing neurons and the brain substrate.
Validation of generated morphologies against population statistics of experimental data.

Main Results:

NeuroMaC successfully generates accurate virtual morphologies for distinct neuron classes.
The framework replicates morphologies both in isolation and within neuronal forests.
Context-aware generation inherently explains the observed variation in neuronal morphologies.

Conclusions:

Considering the developmental context is essential for understanding neuronal morphology and its variance.
NeuroMaC provides a powerful tool for generating realistic neuronal morphologies.
The framework has potential applications in studying healthy/pathological morphologies and large-scale brain modeling.