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

Neural Circuits01:25

Neural Circuits

2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.5K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.5K
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....
3.5K
Somatosensory, Motor, and Association Cortex01:24

Somatosensory, Motor, and Association Cortex

1.9K
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
1.9K
Neurons: The Axon01:21

Neurons: The Axon

6.6K
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.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment....
6.6K
Neuron Structure01:31

Neuron Structure

230.1K
Overview
230.1K
Neuron Structure01:30

Neuron Structure

17.3K
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...
17.3K

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

What can a neuron compute.

bioRxiv : the preprint server for biology·2026
Same author

Dendro-plexing of Single Input Spikes via Multiple Synaptic Contacts Can Enhance Cortical Neuron Computation and Reduce Axonal Wiring.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Spine-neck electrical bottlenecks tune temporal precision and inhibitory gating in cortical pyramidal neurons: A connectomics-based biophysical study.

bioRxiv : the preprint server for biology·2026
Same author

Enhanced Distal Signaling in Human Hippocampal Neurons despite Lower Intrinsic Excitability.

Research square·2025
Same author

Editorial: What makes us human: from genes to machine.

Frontiers in neuroscience·2025
Same journal

Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Frontiers in computational neuroscience·2026
Same journal

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
See all related articles

Related Experiment Video

Updated: Dec 21, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

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

Published on: June 21, 2022

2.1K

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell.

Toviah Moldwin1, Idan Segev1,2

  • 1Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

Frontiers in Computational Neuroscience
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

Cortical pyramidal neurons can perform machine learning tasks, acting as biophysical perceptrons. This research demonstrates their capacity for classification and pattern discrimination, comparable to artificial algorithms.

Keywords:
compartmental modelingcortical excitatory synapsesdendritic voltage attenuationmachine learningnon-linear dendritesperceptronsingle neuron computationsynaptic weights

More Related Videos

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K
Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex
09:55

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Published on: September 5, 2018

8.7K

Related Experiment Videos

Last Updated: Dec 21, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

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

Published on: June 21, 2022

2.1K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K
Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex
09:55

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Published on: September 5, 2018

8.7K

Area of Science:

  • Computational neuroscience
  • Machine learning

Background:

  • Perceptron and backpropagation algorithms are foundational to machine learning.
  • Current algorithms use simplified neuron models, not reflecting real biophysical complexity.

Purpose of the Study:

  • To investigate if realistic biophysical neurons can perform perceptron-like learning.
  • To model a layer 5 cortical pyramidal cell as a biophysical perceptron (BP).

Main Methods:

  • Implemented the perceptron learning algorithm in a detailed biophysical model of a cortical pyramidal neuron.
  • Tested the BP on binary classification and pattern generalization tasks.

Main Results:

  • The biophysical perceptron achieved classification accuracy comparable to the original perceptron.
  • Performance was evaluated on tasks involving 100 to 2,000 patterns and noisy pattern discrimination.
  • The apical tuft showed some limitations in classification capacity.

Conclusions:

  • Cortical pyramidal neurons are capable of functioning as powerful classification devices.
  • Biophysical neurons can implement learning algorithms beyond simplified models.