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Related Concept Videos

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.
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:
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...
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...
Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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...

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Generative model for feedback networks.

Douglas R White1, Natasa Kejzar, Constantino Tsallis

  • 1Institute of Mathematical Behavioral Sciences, University of California Irvine, Irvine, California 92697, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 21, 2006
PubMed
Summary
This summary is machine-generated.

This study introduces a network formation model inspired by real-world growth patterns. Simulations suggest network degree distributions may follow a q-exponential function, useful for modeling complex systems.

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Area of Science:

  • Complex systems
  • Network science
  • Statistical mechanics

Background:

  • Real-world networks like kinship, trading, and chemical reaction networks grow dynamically.
  • Nodes in these networks often lack global information, relying on local search and feedback for growth.
  • Existing network models may not fully capture these decentralized growth mechanisms.

Purpose of the Study:

  • To propose a novel model for network formation that mimics decentralized growth.
  • To investigate the statistical properties, particularly degree distribution, of networks generated by this model.
  • To explore the applicability of q-exponential functions in describing these network properties.

Main Methods:

  • A network growth model is proposed, simulating link additions (creating cycles) or new node additions.
  • The model incorporates a search mechanism where nodes seek connections within their local network neighborhood.
  • Simulations are performed to generate networks and analyze their resulting degree distributions.

Main Results:

  • The simulation results indicate that the degree distribution of the generated networks is consistent with a q-exponential function.
  • This finding suggests a potential link between the proposed network formation process and phenomena modeled by q-exponential distributions.
  • The model successfully imitates decentralized network growth through local interactions and feedback.

Conclusions:

  • The proposed model offers a plausible mechanism for the formation of complex networks with decentralized growth.
  • The q-exponential distribution appears to be a suitable statistical descriptor for the degree distribution in these networks.
  • This research contributes to understanding network evolution and its statistical underpinnings in various scientific domains.