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

Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Transient and Steady-state Response01:24

Transient and Steady-state Response

In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state response.

You might also read

Related Articles

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

Sort by
Same author

Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Hippocampal communication with the anterior olfactory nucleus is necessary for context-dependent odor memory.

Behavioral neuroscience·2026
Same author

A biologically plausible decision-making model based on interacting neural populations.

PloS one·2026
Same author

Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.

Nature human behaviour·2026
Same author

Convergent information flows explain recurring firing patterns in cerebral cortex.

Nature neuroscience·2025
Same author

Deletion of NPAS4 in olfactory bulb principal neurons alters E/I balance and impairs decoding of chemically similar odour molecules.

The Journal of physiology·2025

Related Experiment Videos

State dependence of network output: modeling and experiments.

Farzan Nadim1, Vladimir Brezina, Alain Destexhe

  • 1New Jersey Institute of Technology and Rutgers University, Newark, New Jersey 07102, USA. farzan@njit.edu

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|November 14, 2008
PubMed
Summary
This summary is machine-generated.

Neural network responses vary based on their activity state, influenced by factors like neuromodulation and synaptic input. Understanding these state-dependent dynamics is crucial across various species for deciphering brain function.

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Evidence suggests neural networks and neurons exhibit state-dependent responses to identical inputs.
  • Network states are shaped by intrinsic dynamics, neuromodulation, synaptic input balance, and activity history.
  • Comparisons between awake and sleep states in mammals provide key insights, though underlying mechanisms remain complex.

Purpose of the Study:

  • To explore the computational principles of state-dependent neural network function.
  • To integrate experimental and modeling approaches to understand network state dynamics.
  • To demonstrate the unifying theme of state dependence across diverse biological systems.

Main Methods:

  • Review and synthesis of recent experimental and computational studies.
  • Analysis of state-dependent effects in vertebrate and invertebrate models.
  • Examination of mechanisms including neuromodulation, sensory input, and experience.

Main Results:

  • Identified cellular and synaptic mechanisms contributing to state dependence.
  • Highlighted computational principles emerging from state-dependent network activity.
  • Demonstrated the universality of state-dependent network output across mammalian, crustacean, and mollusk systems.

Conclusions:

  • Network state significantly alters neuronal and network responses to stimuli.
  • Neuromodulation, synaptic balance, and activity history are key determinants of network state.
  • State-dependent processing is a fundamental principle of neural computation across diverse species.