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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Discrete-time Fourier transform01:26

Discrete-time Fourier transform

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Dynamic Equilibrium02:20

Dynamic Equilibrium

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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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相关实验视频

Updated: Jan 29, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
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在前皮层持续活动的基础上,

Hidehiko K Inagaki1, Lorenzo Fontolan1, Sandro Romani2

  • 1Janelia Research Campus, HHMI, Ashburn, VA, USA.

Nature
|February 8, 2019
PubMed
概括

短期记忆对于将过去的感觉与未来的行为联系起来至关重要, 这项研究揭示了小鼠前侧运动皮质 (ALM) 中的离散吸引力,

科学领域:

  • 神经科学
  • 计算神经科学
  • 发动机控制

背景情况:

  • 短期记忆将事件与时间联系在一起,
  • 持续数秒的神经活动是短期记忆和运动规划的标志.
  • 在延迟反应任务中,前侧运动皮层 (ALM) 的神经元表现出持续的活动.

研究的目的:

  • 阐明用于运动规划的短期记忆中的持续神经活动的基本原理.
  • 在延迟反应任务期间调查ALM中神经元群体的动态.
  • 确定吸引力动力学是否控制运动规划中的短期记忆.

主要方法:

  • 在小鼠中结合细胞内和细胞外电生理学.
  • 使用光遗传学乱来操纵神经活动.
  • 使用网络建模来分析神经动态.
  • 从前侧运动皮层 (ALM) 的神经元中记录.

主要成果:

  • 在延迟期间,ALM神经元活动向与特定运动方向相对应的离散"终点"发展.
  • 这些确定的终点显示出对短暂的光遗传乱的强度.
  • 偶尔,扰动会导致状态切换到其他终点,导致错误的操作.

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  • 证据表明吸引力动态控制着观察到的持续活动.
  • 结论:

    • 在运动规划中,离散的吸引力是短期记忆的基础.
    • 随着时间的推移,ALM网络使用这些吸引器来维护与行动相关的信息.
    • 这种机制提供了强度,并允许状态切换,影响行为结果.