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相关概念视频

Propagation of Action Potentials01:23

Propagation of Action Potentials

5.7K
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...
5.7K
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
Classification of Systems-I01:26

Classification of Systems-I

186
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
186
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
Neuronal Communication01:28

Neuronal Communication

900
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...
900
Propagation Speed of Electromagnetic Waves01:30

Propagation Speed of Electromagnetic Waves

3.4K
Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
3.4K

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相关实验视频

Updated: Jul 1, 2025

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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频率传播:非线性物理网络中的多机制学习.

Vidyesh Rao Anisetti1, Ananth Kandala2, Benjamin Scellier3

  • 1Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A. vvaniset@syr.edu.

Neural computation
|March 8, 2024
PubMed
概括

我们开发了频率传播,这是物理网络的新学习算法. 这种方法使用不同的频率来激活和错误信号,为可适应的网络参数实现高效的梯度下降.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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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

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相关实验视频

Last Updated: Jul 1, 2025

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

11.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 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

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科学领域:

  • 物理 物理学 物理
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 传统的机器学习算法通常需要集中处理和全球信息.
  • 物理网络,如电力或流量网络,为分布式计算和学习提供了独特的机会.
  • 开发物理系统的高效,本地学习规则对于其实际应用至关重要.

研究的目的:

  • 为非线性物理网络引入频率传播,一种新的学习算法.
  • 为了证明频率传播能够在损失函数上实现梯度下降.
  • 建立频率传播作为一种适用于各种物理网络的多机制学习策略.

主要方法:

  • 将一个频率的激活电流和另一个频率的误差电流应用于具有可变电阻的电阻电路.
  • 分析了电压响应作为频域中激活和错误信号的叠加.
  • 在本地更新电路导电量,与从激活和错误信号中获得的系数的乘积成比例.

主要成果:

  • 频率传播成功地使用本地信息更新网络参数.
  • 学习规则被证明可以在定义的损失函数上执行梯度下降.
  • 证明了该算法的适用于非线性物理网络,包括电阻,弹性和流量网络.

结论:

  • 频率传播是物理网络的有效和本地学习算法.
  • 该算法是多机制学习策略的典范,利用不同的物理量作为信号.
  • 这种方法为理解各种物理网络学习机制提供了一个统一的框架,包括在流网络中进行的先前工作.