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相关概念视频

Propagation of Action Potentials01:23

Propagation of Action Potentials

6.0K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.0K
Action Potential01:31

Action Potential

8.0K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they...
8.0K
Action Potentials01:41

Action Potentials

131.6K
Overview
131.6K
Graded Potential01:19

Graded Potential

4.1K
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...
4.1K
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

6.1K
The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
6.1K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.8K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
2.8K

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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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参数管理激活函数用于将神经网络潜能与由低维潜能强制执行的物理行为相匹配.

Farideh Badichi Akher1, Yinan Shu1, Zoltan Varga1

  • 1Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.

The journal of physical chemistry. A
|June 12, 2023
PubMed
概括

这项研究引入了一个神经网络的新型激活函数,通过强制物理约束,如消失的相互作用来增强机器学习潜力. 这提高了稀疏数据区域的准确性,以新的臭氧潜在能量表面为例.

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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相关实验视频

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科学领域:

  • 计算化学计算化学
  • 机器学习 机器学习
  • 量子化学 是一个量子化学.

背景情况:

  • 机器学习的潜在能量表面 (PES) 越来越多地被使用,但在数据稀疏的地区,其精度很难达到.
  • 现有的方法通常需要特定的功能形式或广泛的数据来进行可靠的推断.
  • 将物理知识纳入机器学习模型对于提高性能至关重要.

研究的目的:

  • 开发一种方法,将人类智能,特别是物理限制,整合到机器学习潜力中.
  • 解决神经网络输出在培训数据有限的地区的不可靠性问题.
  • 提出一种新的激活函数,在神经网络中强制执行低维限制.

主要方法:

  • 引入了一种新的类型的激活功能,用于feedforward神经网络,该神经网络在参数上取决于所有输入变量.
  • 证明了这种激活函数强制执行物理约束的能力,例如在大型子系统分离时消失的相互作用潜力.
  • 应用了该方法来为臭氧 (O3) 的14个最低3A'状态产生改进的潜在能量表面.
  • 介绍了一种通用方法,即通过深度神经网络 (PM-DDNN) 进行参数式管理的糖尿病化,这是相对于以前的技术的进步.

主要成果:

  • 在没有特定的功能形式或额外的数据的情况下,成功地强制执行了在物理上现实的条件,即在大型子系统分离时的相互作用潜力接近零.
  • 为臭氧生成了一组改进的潜在能量表面,证明了该方法的实际应用.
  • 展示了将各种低维或较低层次的知识纳入机器学习潜力的方法的普遍性.

结论:

  • 开发的激活功能有效地将物理知识集成到机器学习的潜能中,提高了它们的可靠性和推断能力.
  • 该方法提供了一种方便的方法来提高基于神经网络的PES的准确性,特别是在数据稀缺的地区.
  • 一般化的PM-DDNN方法为量子化学应用提供了机器学习的进一步进展.