Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.1K
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....
3.1K
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.4K
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...
1.4K
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Parallax error indicates simple cue-anchoring in the head-direction system.

bioRxiv : the preprint server for biology·2026
Same author

The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Nature communications·2025
Same author

Multi-band oscillations emerge from a simple spiking network.

Chaos (Woodbury, N.Y.)·2023
Same author

C. elegans enteric motor neurons fire synchronized action potentials underlying the defecation motor program.

Nature communications·2022
Same author

Global minimization via classical tunneling assisted by collective force field formation.

Science advances·2021
Same author

Dimensional reduction of emergent spatiotemporal cortical dynamics via a maximum entropy moment closure.

PLoS computational biology·2020
Same journal

Unlocking the capacity of Mn-based Prussian blue cathodes in capacitive deionization.

Nature communications·2026
Same journal

Scaling biodiversity-stability relationships from populations to meta-communities across trophic levels.

Nature communications·2026
Same journal

Thermodynamically programmed one-pot CRISPR platform for point-of-care SNP genotyping.

Nature communications·2026
Same journal

Engineering all-organic electrocatalysts with asymmetric dual-active sites for uncommon oxygen-evolving pathway.

Nature communications·2026
Same journal

Rapid GC content evolution in rice through GC-biased gene conversion and selection for translation efficiency.

Nature communications·2026
Same journal

Declines in organic matter persistence with increased soil carbon.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 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

7.0K

在尖端的神经形态硬件上实现的反向传播算法.

Alpha Renner1,2, Forrest Sheldon3,4, Anatoly Zlotnik5

  • 1Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland.

Nature communications
|November 8, 2024
PubMed
概括
此摘要是机器生成的。

研究人员为神经形态硬件开发了一种新的尖端反向传播算法. 这种芯片上实现实现了机器学习任务的竞争性准确性,为高效的边缘计算应用铺平了道路.

更多相关视频

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

10.3K
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

11.4K

相关实验视频

Last Updated: Jun 8, 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

7.0K
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

10.3K
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

11.4K

科学领域:

  • 神经科学和人工智能 人工智能
  • 神经形态工程的神经形态工程
  • 机器学习 机器学习

背景情况:

  • 自然的神经系统激发了先进的机器学习和神经形态电路.
  • 现代深度学习,特别是反向传播,在神经生理学可信性和硬件实现方面面临挑战.
  • 现有的神经形态方法经常难以复制确切的反向传播算法.

研究的目的:

  • 在英特尔的Loihi研究处理器上实现了一个神经形态的,尖的反向传播算法.
  • 为了证明一条能够进行芯片内学习的三层电路的原理证明.
  • 展示完全在芯片上的尖端神经网络 (SNN) 中精确反向传播的可行性.

主要方法:

  • 实现一个synfire-gated的动态信息协调和处理算法.
  • 在英特尔的Loihi神经形态研究处理器上部署.
  • 在MNIST和时尚MNIST数据集上进行培训和测试,用于数字和服装项目分类.

主要成果:

  • 成功证明了三层尖端神经网络学习任务的原理证明.
  • 获得了与离芯片训练的SNNs相匹敌的分类准确性.
  • 展示了一种适用于边缘计算应用的能量延迟产品.

结论:

  • 这项工作代表了SNN中第一个完全在芯片上,计算机在循环中的精确反向传播算法的首次完全实现.
  • 开发的方法可以在神经形态处理器上实现低功耗,低延迟的深度学习应用程序.
  • 突出了将高级机器学习与内存,大规模并行神经形态硬件集成的可行途径.