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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
129
Types Of Transformers01:16

Types Of Transformers

943
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
943
The Ideal Transformer01:26

The Ideal Transformer

342
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
342
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

464
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
464
Energy Losses in Transformers01:21

Energy Losses in Transformers

819
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
819

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Aligning Transregional Neural Dynamics with Transformer-based Variational Autoencoders.

Shenghui Wu, Xiang Zhang, Yifan Huang

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    Summary

    This study introduces a novel method to predict neural activity between brain regions by analyzing shared latent dynamics. The approach successfully models communication from the medial prefrontal cortex to the motor cortex, aiding future neural prosthetics.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Systems Neuroscience

    Background:

    • Neural spiking activity reveals communication pathways between brain regions.
    • Low-dimensional latent variables model neural activity within single regions.
    • Transregional correlations of latent dynamics remain understudied.

    Purpose of the Study:

    • To develop a unified architecture for analyzing and exploiting latent dynamics correlations between two cortical areas.
    • To predict neural spiking activity from upstream to downstream brain regions.
    • To investigate the relationship between medial prefrontal cortex (mPFC) and primary motor cortex (M1) neural activity.

    Main Methods:

    • Utilized Transformer-based variational autoencoders (tVAEs) to extract latent variables from mPFC and M1 neural spike trains.
    • Employed a regression model to align latent variables from the two brain regions.
    • Cascaded tVAE encoder (mPFC) and decoder (M1) through aligned latent variables for spike prediction.

    Main Results:

    • tVAEs successfully extracted latent dynamics from mPFC and M1 spike ensembles, mirroring behavioral trajectories.
    • Demonstrated shared latent dynamics between mPFC and M1 neural activity.
    • Showcased linear alignment of latent dynamics for effective spike prediction from mPFC to M1.

    Conclusions:

    • The proposed method effectively models transregional latent dynamics and predicts neural activity.
    • Shared latent dynamics between mPFC and M1 can be linearly aligned for spike prediction.
    • This approach offers a valuable tool for studying inter-regional neural communication and designing neural prostheses.