Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neurons: The Cell Body and the Dendrites01:23

Neurons: The Cell Body and the Dendrites

7.8K
A typical nerve cell comprises three main components: the cell body, dendrites, and the axon. The cell body, also known as the soma or perikaryon, serves as the central biosynthetic hub housing a nucleus surrounded by cytoplasm containing organelles commonly found in most cells. Notably, Nissl bodies, clusters of the rough endoplasmic reticulum and free ribosomes responsible for protein synthesis, are distinctive features of the neuronal cell body. As neurons age, aggregates of a brown pigment...
7.8K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.0K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
4.0K
Neuron Structure01:31

Neuron Structure

233.9K
Overview
233.9K
Neuron Structure01:30

Neuron Structure

19.4K
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...
19.4K
The Synapse02:47

The Synapse

134.9K
Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
134.9K
Synaptic Signaling01:09

Synaptic Signaling

6.9K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Ultra-wide-field, deep, adaptive two-photon microscopy for multi-scale neuronal imaging.

Light, science & applications·2026
Same author

Synergy mediates long-range correlations in the visual cortex near criticality.

Frontiers in computational neuroscience·2026
Same author

Astrocytes and neurons exhibit partially shared but distinct composite receptive fields for natural stimuli.

Journal of neurophysiology·2026
Same author

Diagnostic utility of IBEX Bone Health for assessment of osteoporosis from knee radiographs.

Frontiers in molecular biosciences·2025
Same author

Synergy mediates Long-Range Correlations in the Visual Cortex Near Criticality.

bioRxiv : the preprint server for biology·2025
Same author

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Subcellular Patch-clamp Recordings from the Somatodendritic Domain of Nigral Dopamine Neurons
09:17

Subcellular Patch-clamp Recordings from the Somatodendritic Domain of Nigral Dopamine Neurons

Published on: November 2, 2016

15.5K

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity.

Romain D Cazé1, Sarah Jarvis2, Amanda J Foust3

  • 1Center for Neurotechnology and Department of Bioengineering, Imperial College London, London SW7 2AZ, U.K. romain.caze@gmail.com.

Neural Computation
|June 10, 2017
PubMed
Summary
This summary is machine-generated.

Neurons can achieve stimulus selectivity through the spatial arrangement of synapses on dendrites, not just input strength. This dendritic mechanism enhances neural robustness against synaptic and dendritic damage.

More Related Videos

Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function
09:09

Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function

Published on: August 7, 2019

6.6K
Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila
07:13

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Published on: January 7, 2019

14.7K

Related Experiment Videos

Last Updated: Feb 28, 2026

Subcellular Patch-clamp Recordings from the Somatodendritic Domain of Nigral Dopamine Neurons
09:17

Subcellular Patch-clamp Recordings from the Somatodendritic Domain of Nigral Dopamine Neurons

Published on: November 2, 2016

15.5K
Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function
09:09

Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function

Published on: August 7, 2019

6.6K
Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila
07:13

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Published on: January 7, 2019

14.7K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Stimulus selectivity is crucial for sensory information processing in cortical neurons.
  • Existing models often assume selectivity arises from weighted inputs, neglecting dendritic contributions.

Purpose of the Study:

  • To investigate nonlinear dendritic processing as a mechanism for stimulus selectivity.
  • To explore how synaptic distribution on dendrites influences neuronal response.
  • To assess the robustness of this mechanism against neural component failure.

Main Methods:

  • Developed a multi-subunit nonlinear model of dendritic processing.
  • Simulated synaptic input and dendritic integration.
  • Validated model predictions using a layer 2/3 biophysical neuron model.

Main Results:

  • Demonstrated that spatial synapse distribution can generate stimulus selectivity, independent of total input weight.
  • Showed increased robustness to synaptic and dendritic loss compared to linear models.
  • Confirmed consistency with experimental observations regarding dendritic vs. somatic selectivity and hyperpolarization effects.

Conclusions:

  • Nonlinear dendritic processing offers a general mechanism for neuronal stimulus selectivity.
  • This mechanism enhances resilience to neural damage, with implications for neuromorphic computing.
  • The model predicts depolarization-induced selectivity in initially nonselective neurons.