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相关概念视频

Aging01:26

Aging

86
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
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The Effect of Aging on Tissues01:19

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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
236
Resting Potential Decay01:15

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The resting membrane potential of a neuron (-70mV) is sustained due to the selective ion permeability of the membrane. At the resting potential, the membrane is slightly permeable to ions like sodium (Na+) and chloride (Cl−) and highly permeable to potassium ions (K+). Differences in the ions' concentration inside the cell compared to the outside are maintained by membrane transport proteins like channels and pumps.
At rest, the K+ is the main ion that moves across the membrane...
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Current Growth And Decay In RL Circuits01:30

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The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
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Transfer Function to State Space01:23

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
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相关实验视频

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Preparation of Acute Hippocampal Slices from Rats and Transgenic Mice for the Study of Synaptic Alterations during Aging and Amyloid Pathology
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使用回声状态网络预测衰老过渡.

Biswambhar Rakshit1, Aryalakshmi S1, Arjun J Kartha1

  • 1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India.

Chaos (Woodbury, N.Y.)
|August 3, 2023
PubMed
概括
此摘要是机器生成的。

神经网络可以预测合振荡器何时停止工作. 一个简单的回声状态网络 (ESN) 可以准确地预测系统崩,并识别复杂网络中的关键过渡.

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科学领域:

  • 复杂的系统复杂的系统.
  • 非线性动力学是一种非线性动力学.
  • 计算神经科学是一种神经科学.

背景情况:

  • 结合的非线性振荡器可以在不活性元素的关键部分停止活动,这种现象被称为衰老过渡.
  • 对这种过渡的分析预测通常依赖于集群同步观点.
  • 像神经网络这样的无模型,数据驱动的方法来预测这种过渡的潜力仍然是一个开放的问题.

研究的目的:

  • 调查神经网络框架,特别是回声状态网络 (ESN) 在预测合非线性振荡器中衰老过渡的能力.
  • 确定ESN是否能够准确预测时间演变和系统崩的开始.
  • 评估ESN识别关键参数的能力,并在不同的网络拓中进行概括.

主要方法:

  • 使用一个简单的回声状态网络 (ESN) 与训练出来的权重.
  • 训练ESN使用来自合范式极限周期振荡器的数据.
  • 测试了ESN在所有对所有,小世界和无尺度网络拓学的预测能力,使用来自崩前制度或平均场动态的最小训练数据 (两个节点).

主要成果:

  • ESN准确地预测了合振荡器系统的时间演变和崩的开始.
  • 在各种网络结构中,ESN成功地确定了不活跃振荡器的关键部分.
  • 在ESN展示了预测老龄化过渡的能力,即使训练有有限的数据或平均场动态.

结论:

  • 一种无模型,数据驱动的ESN方法可以有效地预测合非线性振荡器系统的衰老过渡.
  • 在没有先前的系统知识的情况下,ESN为预测系统崩和识别关键动态提供了一个强大的工具.
  • 这个框架对分析和预测复杂的动态网络中新出现的现象充满希望.