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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...

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Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
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Related Experiment Video

Updated: Dec 23, 2025

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

Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification.

Netanel Ofer^1,2, Orit Shefi^3,4, Gur Yaari⁵

¹Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.

Neuroinformatics

|April 30, 2020

Summary

This study introduces a new method for classifying neurons by analyzing both their axonal and dendritic trees. Combining biophysical simulations with morphological data significantly improves the accuracy of neuron classification.

Keywords:

Interneuron classification Neuromorphology Neuronal coding

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Last Updated: Dec 23, 2025

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

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Published on: February 15, 2017

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Published on: September 17, 2021

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

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Published on: November 20, 2018

Area of Science:

Neuroscience
Computational Biology
Biophysics

Background:

Neurons exhibit diverse properties, including morphology, electrophysiology, and molecular characteristics.
Current neuron classification methods primarily focus on dendritic tree structure or axonal projection patterns.

Purpose of the Study:

To develop a more robust method for interneuron classification.
To investigate the utility of combining axonal signal propagation patterns with morphological features for neuron classification.

Main Methods:

Utilized data from public databases of neuronal reconstructions and membrane properties.
Performed biophysical simulations to analyze signal propagation along axonal trees.
Integrated axonal signal propagation patterns with morphological parameters of both axonal and dendritic trees.

Main Results:

The combined approach significantly improved classification accuracy compared to previous methods.
Demonstrated the effectiveness of integrating dynamic signal propagation with static morphology for neuron classification.

Conclusions:

The proposed classification schemes offer a robust approach for categorizing neurons.
This work advances the understanding of form-function principles in realistic neuronal models.