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

Effects of feedback01:24

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
Positive and Negative Feedback Loops01:18

Positive and Negative Feedback Loops

Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:
Feedback Loops01:01

Feedback Loops

In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
Exponential Growth01:29

Exponential Growth

Bacterial populations exhibit exponential growth when conditions such as nutrient availability and temperature are favorable. In this phase, cells reproduce through binary fission, where each cell divides into two identical daughter cells. This process causes the population to double at regular intervals, resulting in a growth rate that is directly proportional to the current number of cells. As the population increases, the number of new cells formed during each generation also grows, creating...

You might also read

Related Articles

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

Sort by
Same author

Scalar Fields around Black Hole Binaries in LIGO-Virgo-KAGRA.

Physical review letters·2026
Same author

Developmental toxicity of two organophosphate pesticides in Zebrafish embryo: Comparative and combinatorial assessment of neuro- and cardio-toxicity of sub-lethal concentrations of chlorpyrifos and malathion.

Aquatic toxicology (Amsterdam, Netherlands)·2026
Same author

Coupled catastrophes in systems with bidirectional feedback.

Chaos (Woodbury, N.Y.)·2025
Same author

What does the tree of life look like as it grows? Evolution and the multifractality of time.

Journal of theoretical biology·2025
Same author

Morphological organisation of the digestive tract in the stream catfish Pseudecheneis sulcatus (McClelland).

Micron (Oxford, England : 1993)·2024
Same author

First Constraints on Compact Binary Environments from LIGO-Virgo Data.

Physical review letters·2024
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Axon Stretch Growth: The Mechanotransduction of Neuronal Growth
11:46

Axon Stretch Growth: The Mechanotransduction of Neuronal Growth

Published on: August 10, 2011

Network growth with feedback.

Raissa M D'Souza1, Soumen Roy

  • 1Department of Mechanical and Aeronautical Engineering, University of California, Davis, California 95616, USA. rmdsouza@ucdavis.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new framework for network growth that uses feedback to dynamically adjust parameters. This approach creates larger, more efficient networks compared to fixed-parameter models.

More Related Videos

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

Nutrient Regulation by Continuous Feeding for Large-scale Expansion of Mammalian Cells in Spheroids
11:01

Nutrient Regulation by Continuous Feeding for Large-scale Expansion of Mammalian Cells in Spheroids

Published on: September 25, 2016

Related Experiment Videos

Last Updated: Jun 28, 2026

Axon Stretch Growth: The Mechanotransduction of Neuronal Growth
11:46

Axon Stretch Growth: The Mechanotransduction of Neuronal Growth

Published on: August 10, 2011

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

Nutrient Regulation by Continuous Feeding for Large-scale Expansion of Mammalian Cells in Spheroids
11:01

Nutrient Regulation by Continuous Feeding for Large-scale Expansion of Mammalian Cells in Spheroids

Published on: September 25, 2016

Area of Science:

  • Complex systems
  • Network science
  • Computational modeling

Background:

  • Traditional network growth models often use static parameters, limiting adaptability.
  • Understanding dynamic network evolution is crucial for various fields.

Purpose of the Study:

  • To introduce a general framework for network growth using dynamic parameter adjustment.
  • To analyze a specific model with resource competition and feedback control.

Main Methods:

  • Developed a general framework incorporating feedback loops.
  • Analyzed a specific network model with limited resources and node competition.
  • Employed exact analytical methods to derive results.

Main Results:

  • Tunable feedback mechanisms promote the development of larger and more efficient networks.
  • Linear resource scaling leads to a condensed state, a phenomenon delayed by sublinear scaling.
  • The framework allows for dynamic adaptation of network growth parameters.

Conclusions:

  • Dynamic feedback control offers a superior approach to network growth compared to static models.
  • Resource allocation strategies significantly impact network structure and efficiency.
  • The proposed framework provides a flexible platform for studying adaptive network evolution.