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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

55.2K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.2K
Long-term Depression01:03

Long-term Depression

2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
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Neuroplasticity01:01

Neuroplasticity

344
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
344
First Order Systems01:21

First Order Systems

90
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
90
Second Order systems II01:18

Second Order systems II

108
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
108
Linear time-invariant Systems01:23

Linear time-invariant Systems

254
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
254

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相关实验视频

Updated: Jun 29, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

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可训练的时间延迟 延迟神经网络用于学习 延迟动力学

Xunbi A Ji, Gabor Orosz

    IEEE transactions on neural networks and learning systems
    |March 28, 2024
    PubMed
    概括

    本研究介绍了可训练延迟神经网络 (TrTDNNs),以学习时间延迟系统中的复杂动态. 这些网络可以同时从数据中识别系统非线性和时间延迟.

    科学领域:

    • 控制系统工程 控制系统工程
    • 机器学习 机器学习
    • 动态系统理论 动态系统理论

    背景情况:

    • 时间延迟系统在各种科学和工程领域普遍存在.
    • 学习这些系统的非线性动态,特别是未知的延迟,仍然具有挑战性.
    • 传统的神经网络往往难以模拟时间延迟系统固有的时间依赖性.

    研究的目的:

    • 建立连续时间框架,连接时间延迟系统和时间延迟神经网络 (TDNN).
    • 引入一种具有可训练延迟 (TrTDNN) 的新型 TDNN 架构,用于同时学习动态和延迟.
    • 为TrTDNNs开发训练算法,以便有效地从轨迹数据中学习.

    主要方法:

    • 开发了时间延迟神经网络与可训练延迟 (TrTDNNs) 的概念.
    • 构建训练算法,同时识别非线性和时间延迟.
    • 利用连续时间视角进行建模和学习.

    主要成果:

    • 成功证明了TrTDNN能够从轨迹数据中学习非线性动态的能力.
    • 通过模拟数据验证了在自主系统上提出的技术.
    • 应用并验证了用于连接自动化车辆 (CAV) 纵向动态实验数据的方法.

    更多相关视频

    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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    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

    Published on: June 24, 2015

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    相关实验视频

    Last Updated: Jun 29, 2025

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.0K
    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

    9.9K
    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
    08:08

    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

    Published on: June 24, 2015

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    结论:

    • TrTDNN提供了一种强大的方法来建模和学习时间延迟系统中复杂的动态.
    • 同时学习非线性和时间延迟是可行的和有效的.
    • 这种方法对自动驾驶系统和互联汽车的应用非常有希望.