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相关概念视频

The Synapse02:47

The Synapse

124.7K
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.
124.7K
Neuronal Communication01:28

Neuronal Communication

828
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
828
Overview of Synapses01:25

Overview of Synapses

2.2K
A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
2.2K
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
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Electrical Synapses01:28

Electrical Synapses

8.3K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
8.3K

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相关实验视频

Updated: Jun 21, 2025

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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平行突触与传输非线性增强神经元分类能力

Yuru Song1, Marcus K Benna2

  • 1Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.

bioRxiv : the preprint server for biology
|July 15, 2024
PubMed
概括

皮层神经元通过非线性并行突触获得增强的计算能力. 这种模型显著提高了分类能力,超出了传统方法,即使每个轴突只有很少的突触.

科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习

背景情况:

  • 皮层神经元利用多个突触接触每一个后突触神经元.
  • 通过平行突触的独特计算特性,可以避免功能冗余.

研究的目的:

  • 模拟和评估皮质神经元中非线性并行突触提供的计算增强.
  • 评估突触数和可学习参数对神经元分类能力的影响.

主要方法:

  • 以可学习参数 (广度,斜率,值) 的信号传输函数作为突触电流的建模.
  • 与感知器相比,评估具有非线性并行突触的神经元分类能力.
  • 将模型应用于用于MNIST图像分类的feedforward神经网络.

主要成果:

  • 具有非线性并行突触的神经元比感知器显著提高了分类能力.
  • 分类准确度随着前突触轴突数量的增加而超线性增加.
  • 模型神经元可以实现任意的单调聚合传输函数.
  • 应用到MNIST分类证明了测试准确度的提高.

结论:

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Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function

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

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  • 每个输入轴突的多个非线性突触显著提高神经元的计算能力.
  • 这种突触架构为神经计算和学习提供了一个强大的机制.
  • 该模型提供了对生物和人工神经网络有效信息处理的见解.