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...
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...
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:
Second-order Op Amp Circuits01:19

Second-order Op Amp Circuits

Implementing second-order low-pass filters in audio systems is crucial in refining audio signals by eliminating undesirable high-frequency noise. These filters typically involve second-order op-amp circuits configured as voltage followers, encompassing two nodes with distinct storage elements.
The analysis of such circuits follows a systematic approach, similar to the second-order RLC circuits. In practical scenarios, bulky inductors are rarely employed due to their size and weight. This means...
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...
Applications of RC Circuits01:22

Applications of RC Circuits

A relaxation oscillator is one of the applications of RC circuits. A neon lamp relaxation oscillator comprises a capacitor, a resistor, a voltage source, and a lamp. The lamp acts like an open circuit, with infinite resistance until the potential difference across the lamp reaches a specific voltage. At that voltage, the lamp acts like a short circuit with zero resistance, and the capacitor discharges through the lamp, thus producing light. Once the capacitor is fully discharged through the...

You might also read

Related Articles

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

Sort by
Same author

Monotone bifurcation graphs.

Journal of biological dynamics·2012
Same author

Proximity of intracellular regulatory networks to monotone systems.

IET systems biology·2008
Same author

Some new directions in control theory inspired by systems biology.

Systems biology·2006
Same author

Methods of robustness analysis for Boolean models of gene control networks.

Systems biology·2006
Same author

Parameter estimation in models combining signal transduction and metabolic pathways: the dependent input approach.

Systems biology·2006
Same author

Neural systems as nonlinear filters.

Neural computation·2000
Same journal

Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation.

IET systems biology·2026
Same journal

scGMB: A scRNA-seq Cell Classification Method Combining GCN and Mamba.

IET systems biology·2026
Same journal

Identification of Chemokine-Related Genes Derived From T and NK Cells in the Tumour Microenvironment of Ovarian Cancer Based on scRNA-Seq.

IET systems biology·2026
Same journal

Unravelling the Mechanism of Compound Kushen Injection in Treating Cervical Cancer Through Ferroptosis Regulation: An Integrated Network Pharmacology and Molecular Docking Study.

IET systems biology·2026
Same journal

Metabolic Reprogramming in Recurrent Spontaneous Abortion: Key Biomarkers Identification and Diagnostic Model Development.

IET systems biology·2026
Same journal

Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Salvianolic Acid B in Reversing Oxaliplatin Resistance of Colorectal Cancer Cells.

IET systems biology·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

Establishing an Octopus Ecosystem for Biomedical and Bioengineering Research
09:10

Establishing an Octopus Ecosystem for Biomedical and Bioengineering Research

Published on: September 22, 2021

Remarks on feedforward circuits, adaptation, and pulse memory.

E D Sontag1

  • 1Rutgers University, Department of Mathematics, New Brunswick, NJ 08903, USA. sontag@math.rutgers.edu

IET Systems Biology
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

Feedforward circuits in biological systems can perfectly adapt to step signals. However, these circuits exhibit memory, preventing adaptation to pulse signals, a phenomenon quantified in this study.

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 17, 2026

Establishing an Octopus Ecosystem for Biomedical and Bioengineering Research
09:10

Establishing an Octopus Ecosystem for Biomedical and Bioengineering Research

Published on: September 22, 2021

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:

  • Systems biology
  • Theoretical biology
  • Biophysics

Background:

  • Biological systems often require precise responses to environmental changes.
  • Feedforward circuits are proposed as mechanisms for achieving perfect adaptation.
  • Understanding adaptation dynamics is crucial for deciphering biological control.

Purpose of the Study:

  • To analyze feedforward circuits as models for perfect adaptation to step signals.
  • To develop a general theoretical framework for studying adaptation in biological systems.
  • To investigate the limitations of these circuits in responding to different signal types.

Main Methods:

  • Development of a general mathematical framework for feedforward circuits.
  • Proof of a global convergence theorem for adaptation to step signals.
  • Analysis of circuit behavior under pulse signal inputs.

Main Results:

  • A general framework is established, encompassing existing models.
  • Perfect adaptation to step signals is proven theoretically.
  • A memory phenomenon was identified, hindering adaptation to pulse signals.
  • The magnitude of this memory effect was estimated.

Conclusions:

  • Feedforward circuits provide a robust model for perfect adaptation to step stimuli.
  • The inherent memory in these circuits leads to a failure in adapting to pulse signals.
  • This study quantifies the memory effect, offering insights into biological signal processing limitations.