Jove
Visualize
Contact Us

Related Concept Videos

Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

191
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
191
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

2.0K
The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
2.0K
Neural Regulation01:37

Neural Regulation

39.1K
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.
39.1K
Parallel Resonance01:23

Parallel Resonance

187
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
187

You might also read

Related Articles

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

Sort by
Same author

Laser systems with a semiconductor optical amplifier and optical-power-to-electrical-current feedback.

Optics letters·2025
Same author

Bismuth-doped fiber amplifier for full S-band amplification.

Optics letters·2025
Same author

Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform.

Nanophotonics (Berlin, Germany)·2025
Same author

Mode structure evolution of a modeless multiwavelength Raman fiber laser.

Optics letters·2025
Same author

Sparse intensity sampling for ultrafast full-field reconstruction in low-dimensional photonic systems.

Communications physics·2025
Same author

On the theory of bi-chromatic pulsed ring lasers with synchronized transient Raman amplification.

Optics letters·2025
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 Experiment Video

Updated: Jun 7, 2025

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

10.3K

Robust neural networks using stochastic resonance neurons.

Egor Manuylovich1, Diego Argüello Ron2, Morteza Kamalian-Kopae2

  • 1Aston Institute of Photonic Technologies, Aston University, Birmingham, UK. e.manuylovich@aston.ac.uk.

Communications Engineering
|November 13, 2024
PubMed
Summary

This study introduces a novel physics-inspired neural network utilizing stochastic resonance. This approach significantly reduces neuron count and enhances noise robustness for improved machine learning performance.

More Related Videos

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
00:06

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.1K
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

7.0K

Related Experiment Videos

Last Updated: Jun 7, 2025

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

10.3K
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
00:06

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.1K
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

7.0K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Deep artificial neural networks offer advanced capabilities but face challenges with computational complexity, power consumption, and signal processing latency.
  • Analog neural networks can reduce power consumption but suffer from noise aggregation, limiting their performance.
  • Physics-inspired machine learning offers potential solutions to overcome limitations in conventional neural network architectures.

Purpose of the Study:

  • To propose a novel neural network architecture inspired by physics principles.
  • To leverage stochastic resonance as a dynamic nonlinear node to enhance network efficiency.
  • To demonstrate a reduction in required neurons and improved robustness against noise.

Main Methods:

  • Development of a new neural network model incorporating stochastic resonance as a core component.
  • Evaluation of the network's predictive accuracy and neuron count requirements.
  • Comparative analysis of noise robustness against conventional neural networks using training data.

Main Results:

  • The proposed neural network significantly reduces the number of neurons needed for a specific prediction accuracy.
  • The network demonstrates enhanced robustness against noise present in training data compared to traditional networks.
  • Stochastic resonance effectively functions as a dynamic nonlinear node, improving network efficiency.

Conclusions:

  • Physics-inspired neural networks utilizing stochastic resonance offer a promising alternative to conventional deep learning models.
  • This approach addresses critical issues of computational complexity and power consumption.
  • The enhanced noise robustness makes these networks suitable for applications with noisy data environments.