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

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...
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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.
Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...
Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

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

Sort by
Same author

Blur identification by multilayer neural network based on multivalued neurons.

IEEE transactions on neural networks·2008
Same author

Temporal classification of Drosophila segmentation gene expression patterns by the multi-valued neural recognition method.

Mathematical biosciences·2002
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Periodic activation function and a modified learning algorithm for the multivalued neuron.

Igor Aizenberg1

  • 1Texas A&M University-Texarkana, 75505, USA. igor.aizenberg@tamut.edu

IEEE Transactions on Neural Networks
|November 5, 2010
PubMed
Summary

A new periodic activation function enhances the multivalued neuron (MVN), creating the MVN-P. This improved neuron efficiently solves complex classification problems, including those previously unsolvable by single neurons.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Jun 7, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Multivalued neurons (MVNs) utilize complex-valued weights and inputs/outputs on the unit circle.
  • While effective, standard MVNs have limitations in learning highly nonlinear functions.
  • Existing MVN-based neural networks show potential but require enhancements for complex tasks.

Purpose of the Study:

  • Introduce a novel periodic activation function for multivalued neurons.
  • Develop a new neuron model, the multivalued neuron with a periodic activation function (MVN-P).
  • Enhance the learning capabilities of single neurons for complex, nonlinear problems.

Main Methods:

  • Designed a new periodic activation function for multivalued neurons.
  • Developed the multivalued neuron with a periodic activation function (MVN-P) model.
  • Proposed an error-correction learning algorithm tailored for the MVN-P.

Main Results:

  • The MVN-P can learn nonlinearly separable problems and non-threshold multiple-valued functions.
  • A single MVN-P effectively solves benchmark classification problems previously deemed unsolvable.
  • The MVN-P demonstrates significantly higher functionality and efficiency in classification tasks compared to regular MVNs.

Conclusions:

  • The MVN-P offers a substantial advancement over standard MVNs, particularly for complex nonlinear functions.
  • The MVN-P provides a unified framework, encompassing universal binary neurons and regular MVNs as special cases.
  • This research opens new avenues for tackling challenging classification problems with enhanced single-neuron models.