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相关概念视频

Integration of Synaptic Events01:28

Integration of Synaptic Events

1.5K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
1.5K
Long-term Potentiation01:25

Long-term Potentiation

2.8K
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.
Hebbian LTP
LTP can occur when...
2.8K
The Synapse02:47

The Synapse

124.9K
Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
124.9K
Chemical Synapses01:26

Chemical Synapses

8.8K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
8.8K
Neural Circuits01:25

Neural Circuits

1.2K
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...
1.2K
Synaptic Signaling01:09

Synaptic Signaling

5.5K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
5.5K

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Spiking Neural Membrane Systems with Multiplexed Neurons for Enhanced Parallel Computing.

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Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes.

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Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses.

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相关实验视频

Updated: Jun 27, 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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尖端神经膜系统具有适应性突触时间延迟.

Yongshun Shen1, Xuefu Liu1, Zhen Yang1

  • 1College of Business, Shandong Normal University, Jinan 250014, P. R. China.

International journal of neural systems
|May 6, 2024
PubMed
概括

尖端神经P系统 (SNP系统) 通过结合星球细胞,增强了自适应性突触时间延迟 (ADSNP系统). 这个模型更好地模拟了生物神经网络,并证明了图灵对计算的普遍性.

科学领域:

  • 计算神经科学是一种计算神经科学.
  • 理论计算机科学理论计算机科学
  • 生物启发的计算是生物启发的

背景情况:

  • 尖端神经P系统 (SNP系统) 是以并行性和时间编码而闻名的计算模型.
  • 最初的SNP系统没有考虑突触传输延迟,限制了它们的生物现实性.
  • 适应性调节突触延迟对于神经系统中准确的时间编码至关重要.

研究的目的:

  • 提出和研究具有适应性突触时间延迟 (ADSNP系统) 的尖端神经膜系统.
  • 通过结合天体细胞介导的适应性突触延迟来增强计算模型.
  • 证明ADSNP系统的图灵通用性和实际可行性.

主要方法:

  • 将能够产生腺三酸盐 (ATP) 的星球细胞引入SNP系统框架.
  • 天体细胞将接收到的尖峰转化为ATP,调节突触时间延迟.
  • 数学证明数字生成和接受模式中的图灵普遍性.

主要成果:

  • ADSNP系统的图灵通用性已经被正式证明.
  • 构建了一个具有93个神经元和星球细胞的小型通用ADSNP系统.
  • ADSNP系统在六种现有变体上表现出优势.
关键词:
膜计算的使用.膜系统是一种膜系统.刺激神经P系统的神经P系统图灵的普遍性 图灵的普遍性

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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

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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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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

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相关实验视频

Last Updated: Jun 27, 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

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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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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

Published on: May 29, 2017

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  • 成功实施了一种用于检测信用卡欺诈的实际应用.
  • 结论:

    • 通过包括自适应性突触延迟,ADSNP系统提供了一个更具生物学可信性的神经信息处理模型.
    • 改进后的模型提高了神经P系统的适应性和时间控制.
    • ADSNP系统可用于现实世界的应用,例如欺诈检测.