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

Nervous Tissue: Neuron Types

Nervous Tissue: Neuron Types

Neurons, the fundamental units of the nervous system, can be classified based on both their structural and functional characteristics.
Structurally, neurons are categorized into three main types: multipolar, bipolar, and unipolar (or pseudounipolar). Multipolar neurons, which are the most common type in the brain and spinal cord, as well as all motor neurons, possess multiple dendrites and a single axon.
Bipolar neurons, on the other hand, have one primary dendrite and one axon. They are...

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 cellular...

You might also read

Related Articles

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

Sort by

Same author

Connectivity Logic of Dendritic Spines in Cortex: Increased Inputs and Ensemble Formation.

bioRxiv : the preprint server for biology·2026

Same author

Screwworm reemergence, illegal cattle movements, and emerging risks to wildlife and protected areas in Mesoamerica.

Conservation biology : the journal of the Society for Conservation Biology·2026

Same author

VesiclePy: A machine learning vesicle analysis toolbox for volume electron microscopy.

PLoS computational biology·2026

Same author

Dense and distributed neuropeptide network in the nerve net of Hydra vulgaris.

PLoS computational biology·2026

Same author

Strengthening biodiversity conservation and One Health through ranger monitoring of wildlife health in protected areas.

Conservation biology : the journal of the Society for Conservation Biology·2026

Same author

Neuronal ensembles in cortical function and disease.

Physiological reviews·2026

Same journal

The Ameliorative Effect of Spirulina platensis as Add-On Therapy to Risperidone on Valproic Acid-Induced Autism in Rat Pups: Implication of Oxidative Stress, Inflammatory, and ERK-1/2 Signaling Pathway.

Developmental neurobiology·2026

Same journal

From Behavioral and Sleep Disturbances to Genetic Diagnosis: Smith-Magenis Syndrome and the Importance of the Diagnostic Pathway.

Developmental neurobiology·2026

Same journal

Is There Any Difference in the Novel Serum Inflammatory Biomarkers of Adolescents With DMDD and Bipolar Disorder?

Developmental neurobiology·2026

Same journal

An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

Developmental neurobiology·2026

Same journal

Disrupted Vestibular Nuclei Neuron Development in a Chick Model for Congenital Vestibular Disorders.

Developmental neurobiology·2026

Same journal

Association Analysis Between HEI-2020 Index and Maternal Pregnancy Behaviors on ADHD in Adolescent Populations: A NHANES Cross-Sectional Study.

Developmental neurobiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

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

Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.

Luis Guerra¹, Laura M McGarry, Víctor Robles

¹Departamento de Inteligencia Artificial, Facultad de Informatica, Universidad Politécnica de Madrid, Spain. l.guerra@upm.es

Developmental Neurobiology

|December 15, 2010

Summary

This summary is machine-generated.

Supervised classification algorithms accurately distinguish neuronal cell types, like pyramidal cells and interneurons, using morphology. This method leverages prior knowledge for objective classification, outperforming unsupervised approaches in neuroscience research.

More Related Videos

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

Published on: July 7, 2023

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Related Experiment Videos

Last Updated: Jun 6, 2026

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

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

Published on: July 7, 2023

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Neuroscience
Computational Biology
Cellular Morphology

Background:

Classifying neuronal cell types is crucial for understanding neural circuits.
Traditional qualitative methods are insufficient; quantitative approaches like unsupervised clustering have limitations.
Accurate classification of neocortical GABAergic interneurons remains challenging.

Purpose of the Study:

To explore supervised classification algorithms for objective neuronal cell type classification based on morphology.
To compare the performance of supervised methods against unsupervised hierarchical clustering.
To identify key morphological features that distinguish neuronal cell types.

Main Methods:

Utilized a database of 128 pyramidal cells and 199 interneurons from mouse neocortex.
Employed supervised classification algorithms and hierarchical clustering.
Defined ground truth for distinguishing pyramidal cells and interneurons by the presence/absence of an apical dendrite.

Main Results:

Supervised classification algorithms significantly outperformed hierarchical clustering.
Feature selection improved classification accuracy for both supervised and unsupervised methods.
Dendritic morphological features were more discriminative than somatic or axonal features for distinguishing pyramidal cells from interneurons.

Conclusions:

Supervised classification is superior for neuronal cell type identification when prior information is available.
Morphological analysis, particularly dendritic features, provides objective criteria for classifying pyramidal cells and interneurons.
Developed methods for automatic distinction of neocortical pyramidal cells and interneurons based on morphology.