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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
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Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...

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Related Experiment Video

Updated: May 9, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

A generative spike train model with time-structured higher order correlations.

James Trousdale1, Yu Hu, Eric Shea-Brown

  • 1Department of Mathematics, University of Houston Houston, TX, USA.

Frontiers in Computational Neuroscience
|August 3, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for correlated neural spike trains, aiding the understanding of neural ensemble dynamics and function. The generalized thinning and shift (GTaS) model generates realistic spike patterns for research.

Keywords:
correlationscumulantneuronal modelingneuronal network modelneuronal networkspoint processesspiking neurons

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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

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Related Experiment Videos

Last Updated: May 9, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Systems Neuroscience

Background:

  • Advanced technologies reveal large-scale neural ensemble activity.
  • Neural spike train correlations are prevalent but poorly understood.
  • Investigating these correlations is crucial for understanding neural dynamics and function.

Purpose of the Study:

  • To introduce a novel generative model for correlated neural spike trains.
  • To demonstrate the model's ability to capture diverse temporal correlation structures.
  • To provide an analytically tractable framework for studying neural ensemble activity.

Main Methods:

  • Developed the generalized thinning and shift (GTaS) model, extending mathematical finance techniques.
  • Generated marginally Poisson spike trains with varied correlation patterns.
  • Derived analytical expressions for cumulant densities of all orders.

Main Results:

  • The GTaS model successfully replicates diverse temporal correlation structures in spike trains.
  • Demonstrated the model's utility in analyzing neural network responses to structured inputs.
  • Established the analytical tractability of the GTaS model.

Conclusions:

  • The GTaS model offers a flexible and powerful tool for neuroscience research.
  • Facilitates the experimental and theoretical exploration of neural dynamics.
  • Enhances understanding of how neural correlations influence ensemble function.