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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

54.9K
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.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Graded Potential01:19

Graded Potential

3.7K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.2K
Long-term Depression01:05

Long-term Depression

30.7K
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.
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相关实验视频

Updated: Jun 13, 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

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基于 Spiking 神经网络中的时间编码来延迟学习.

Pengfei Sun1, Jibin Wu2, Malu Zhang3

  • 1Department of Information Technology, WAVES Research Group, Ghent University, Gent, Belgium.

Neural networks : the official journal of the International Neural Network Society
|September 11, 2024
PubMed
概括
此摘要是机器生成的。

基于时间编码 (DLTC) 的延迟学习优化了尖端神经网络 (SNN) 通过调整尖端时间,而不仅仅是连接重量. 这种新的方法提高了SNN在现实应用中的准确性和效率.

关键词:
推迟学习,延迟学习.尖的神经网络的神经网络.监督学习学习 监督学习时间编码时间编码.

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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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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相关实验视频

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

  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 尖端神经网络 (SNN) 在类似于大脑的信息处理方面表现有前途.
  • 目前的SNN研究主要集中在体重调整上,忽视了关于尖峰时间的重要性的生物学证据.

研究的目的:

  • 引入基于时间编码 (DLTC) 的延迟学习,以优化SNN中的尖端时间.
  • 为了提高SNN的表现,超越传统的基于体重的学习.

主要方法:

  • DLTC集成了可学习的延迟转移来赋予信息的重要性.
  • 一个可调节的值调节神经元发射时间,以精确的尖峰时间.
  • 在视觉和听觉分类任务上测试了DLTC.

主要成果:

  • DLTC的表现始终优于传统的仅重量级SNN.
  • 在精度和计算效率方面取得了显著的改进.
  • 在各种分类任务中证明了有效性.

结论:

  • 通过优化尖端时间,DLTC代表了SNNs的重大进步.
  • 这种方法使SNN更接近现实世界的适用性.
  • DLTC为SNN开发提供了一个新的范式.