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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

459
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
459
Signal and System01:26

Signal and System

1.7K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.7K
Feedback control systems01:26

Feedback control systems

789
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...
789
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.9K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.9K
Integrator and Differentiator01:13

Integrator and Differentiator

1.6K
Op-amp circuits have significant applications in various fields, including automotive engineering. One such application is cruise control systems in cars, where op-amp circuits are integral for maintaining a constant speed. In these systems, op-amps function as both integrators and differentiators.
An integrator within an op-amp circuit produces an output directly proportional to the integral of the input signal. This is achieved by replacing the feedback resistor in a typical inverting...
1.6K
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence.

eLife·2026
Same author

Prefrontal to ventral tegmental area dynamics drive contingency degradation.

Nature·2026
Same author

Single-cell sequencing of rodent ventral pallidum reveals diverse neuronal subtypes with noncanonical interregional continuity.

Science advances·2025
Same author

TSC tunes progenitor balance and upper-layer neuron generation in neocortex.

Nature·2025
Same author

Opioid-driven disruption of the septum reveals a role for neurotensin-expressing neurons in withdrawal.

Neuron·2025
Same author

Heterogeneous pericoerulear neurons tune arousal and exploratory behaviours.

Nature·2025
Same journal

A viral ORFeome library for systems-level genetic dissection of host-pathogen interactions.

Cell·2026
Same journal

Co-option of lysosomal machinery shapes the evolution of the intracellular photosymbiosis supporting coral reefs.

Cell·2026
Same journal

LEF1 and niche factors determine T cell stemness across chronic diseases.

Cell·2026
Same journal

Recurrent patterns of TOP1-mediated neuronal genomic damage shared by major neurodegenerative disorders.

Cell·2026
Same journal

Four-dimensional molecular mapping from a spatial snapshot reveals the dynamics of hair follicle organogenesis.

Cell·2026
Same journal

Whole-cell particle-based digital twin simulations from 4D lattice light-sheet microscopy data.

Cell·2026
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K

Multimodal Signal Integration for Feeding Control.

Marcus L Basiri1, Garret D Stuber2

  • 1Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Cell
|April 23, 2016
PubMed
Summary
This summary is machine-generated.

Researchers identified a neural circuit in fruit flies that controls feeding behavior. This circuit integrates taste information with hunger signals to regulate how much food is consumed.

More Related Videos

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.8K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.9K

Related Experiment Videos

Last Updated: Mar 22, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K
Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.8K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.9K

Area of Science:

  • Neuroscience
  • Behavioral Biology
  • Genetics

Background:

  • Feeding is a fundamental behavior essential for survival across species.
  • Understanding the neural mechanisms regulating feeding is crucial for addressing metabolic disorders.

Purpose of the Study:

  • To identify the neural circuit responsible for integrating gustatory input and hunger state in Drosophila.
  • To elucidate how this circuit modulates food ingestion.

Main Methods:

  • Development of a novel real-time feeding assay in Drosophila melanogaster.
  • Utilized genetic and neurobiological techniques to map neural circuits.

Main Results:

  • Identified a specific neural circuit that processes gustatory cues and internal hunger signals.
  • Demonstrated that this circuit dynamically regulates the initiation and cessation of feeding.

Conclusions:

  • The identified neural circuit provides a key mechanism for adaptive feeding behavior in Drosophila.
  • This study offers insights into the conserved neural basis of appetite regulation.