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

Newton's First Law: Introduction01:17

Newton's First Law: Introduction

22.2K
Motion draws our attention. Motion itself can be beautiful, causing us to marvel at the forces needed to create spectacular sights, such as that of a dolphin jumping out of the water, the flight of a bird, or the orbit of a satellite. The study of motion is kinematics, but kinematics only describes the way objects move—their velocity and acceleration. Dynamics considers the forces that affect the motion of moving objects and systems. Newton's laws of motion are the foundation of...
22.2K
Principle of Equivalence01:18

Principle of Equivalence

2.1K
According to Albert Einstein (1897-1955), free-falling and feeling weightless are intrinsically linked. If a person were in free-fall under gravity, for example, diving towards the Earth from an airplane, they would feel completely weightless. Similarly, a person descending in a lift may feel partially weightless. Broadly speaking, it is assumed that an object in a uniform gravitational field and an object undergoing constant acceleration in the absence of gravity are under the same...
2.1K
Pascal's Law01:04

Pascal's Law

7.9K
In 1653, the French philosopher and scientist Blaise Pascal published "Treatise on the Equilibrium of Liquids," which discussed the principles of static fluids. A static fluid is a fluid that is not in motion. When a fluid is not flowing, we say that the fluid is in static equilibrium. If the fluid is water, we say it is in hydrostatic equilibrium. For a fluid in static equilibrium, the net force on any part of the fluid must be zero; otherwise, the fluid will start to flow. Pascal...
7.9K
Newton's First Law: Application01:12

Newton's First Law: Application

13.7K
Experience suggests that an object at rest remains at rest if left alone, and that an object in motion tends to slow down and stop unless some effort is made to keep it moving. However, Newton's first law gives a deeper explanation of this observation. The study of Newton's laws is like recognizing patterns in nature from which further patterns can be discovered. The genius of Galileo, who first developed the idea for the first law of motion, and Newton, who clarified it, was to ask the...
13.7K
Ratio Level of Measurement00:54

Ratio Level of Measurement

17.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.1K
Introduction to force01:25

Introduction to force

433
Consider water flowing from a nozzle to a turbine vane. As the water hits the turbine vane, it exerts a force that causes it to move along the flow of direction. Force is an impact that changes an object's motion, shape, or orientation. Forces can be caused by physical contact, such as a push or pull, or through non-contact interactions, such as magnetic or gravitational forces. Force is a vector quantity with both magnitude and direction, and is measured in newtons (N) in the SI unit...
433

You might also read

Related Articles

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

Sort by
Same author

Ab initio triplet-triplet annihilation rates for phosphorescent OLED emitters.

The Journal of chemical physics·2026
Same author

Gradient descent in materia through homodyne gradient extraction.

Nature communications·2025
Same author

Analogue speech recognition based on physical computing.

Nature·2025
Same author

Author Correction: Classification with a disordered dopant atom network in silicon.

Nature·2025
Same author

When Machine Learning Meets 2D Materials: A Review.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Toward a formal theory for computing machines made out of whatever physics offers.

Nature communications·2023

Related Experiment Video

Updated: May 14, 2025

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

3.5K

1/f Noise and Machine Intelligence in a Nonlinear Dopant Atom Network.

Tao Chen1, Peter A Bobbert1,2, Wilfred G van der Wiel1

  • 1NanoElectronics Group MESA+ Institute for Nanotechnology and BRAINS Center for Brain-Inspired Nano Systems University of Twente PO Box 217 Enschede AE 7500 The Netherlands.

Small Science
|April 11, 2025
PubMed
Summary
This summary is machine-generated.

This study investigates the 1/f noise in silicon dopant networks, finding optimal signal-to-noise ratios (SNR) that enhance material learning capabilities for physical computing systems.

Keywords:
1/f noisebraincriticalityintelligencenetworknonlinearity

More Related Videos

Real-Time Void Spot Assay
06:39

Real-Time Void Spot Assay

Published on: February 10, 2023

1.8K
Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
07:51

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

8.2K

Related Experiment Videos

Last Updated: May 14, 2025

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

3.5K
Real-Time Void Spot Assay
06:39

Real-Time Void Spot Assay

Published on: February 10, 2023

1.8K
Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
07:51

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

8.2K

Area of Science:

  • Physics
  • Materials Science
  • Computational Neuroscience

Background:

  • Noise is ubiquitous in physical systems, influencing system behavior and understanding.
  • Disordered dopant atom networks in silicon exhibit nonlinear electronic properties suitable for 'material learning' tasks.
  • Understanding noise origins and characteristics is crucial for system analysis and computational applications.

Purpose of the Study:

  • Investigate the intrinsic 1/f noise in silicon dopant networks arising from Coulomb interactions.
  • Analyze the impact of this noise on nonlinearity and signal-to-noise ratio (SNR), key features for computational abilities.
  • Provide guidelines for scaling physical learning machines and offer insights into neuroscience.

Main Methods:

  • Characterization of intrinsic 1/f noise in disordered dopant atom networks.
  • Analysis of Coulomb interactions as the source of 1/f noise.
  • Evaluation of the influence of noise on network nonlinearity and SNR.

Main Results:

  • The study quantifies the impact of 1/f noise on the computational features of silicon dopant networks.
  • Optimal SNR levels were identified that enhance the performance of material learning.
  • Nonlinear data transformation capabilities of the network were analyzed in the context of noise.

Conclusions:

  • The findings offer a new perspective on physical learning machines and their scalability.
  • Understanding noise characteristics is vital for optimizing computational performance in material learning systems.
  • The research provides insights relevant to both condensed matter physics and neuroscience.