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

Neuroplasticity01:01

Neuroplasticity

367
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
367
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
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....
3.2K
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Long-term Potentiation01:35

Long-term Potentiation

55.2K
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.
55.2K

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

Emerging roles of POLR2L of RNA polymerase II dynamics and disease mechanisms (Review).

Molecular medicine reports·2026
Same author

Nurses' perceptions of gerontological nurse practitioner programme curricula: A qualitative descriptive study.

Journal of Korean gerontological nursing·2026
Same author

ERLIN1: A central regulator of protein quality control, lipid homeostasis, and cellular signaling at the endoplasmic reticulum.

Cellular signalling·2025
Same author

Multilevel Nanoarray Spin-Orbit Torque Device for Process-in-Memory Applications.

Nano letters·2025
Same author

Therapeutic potential of sulfasalazine for sarcopenia: Insights from mouse models and clinical data.

Experimental gerontology·2025
Same author

Surrogate Decision-Making Practices Regarding End-of-Life Care for People With Dementia in Long-Term Care Hospitals: A Qualitative Descriptive Study.

Journal of advanced nursing·2025

相关实验视频

Updated: Jul 8, 2025

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K

在时间域中进行神经元修剪,以实现节能SNN处理器设计.

Dongwoo Lew1, Hoyoung Tang1, Jongsun Park1

  • 1School of Electrical Engineering, Korea University, Seoul, Republic of Korea.

Frontiers in neuroscience
|December 15, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种输入依赖计算减少方法,用于尖端神经网络 (SNN). 它修剪不重要的神经元,以显著降低能耗,并加快SNN处理器的速度,而不会失去准确性.

关键词:
这是一个近似的近似.计算 减少 减少取决于输入的神经元修剪.这是一个神经形态神经形态的神经形态.尖的神经网络的神经网络.

更多相关视频

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
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

7.1K

相关实验视频

Last Updated: Jul 8, 2025

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K
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
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

7.1K

科学领域:

  • 神经形态工程的神经形态工程
  • 人工智能的人工智能
  • 计算机架构 计算机架构

背景情况:

  • 深度卷积尖端神经网络 (SNN) 通过结合卷积神经网络 (CNN) 参数来实现高精度.
  • 深度SNN的高计算需求阻碍了节能处理器的设计,这是一个重要的瓶.
  • 现有的SNN需要优化以减少计算,以便在功率受限的应用中广泛部署.

研究的目的:

  • 为深 convolutional SNNs开发一个输入依赖的计算减少方法.
  • 在不影响分类准确性的情况下,识别和修剪不那么重要的神经元.
  • 为了提高SNN硬件的能源效率和处理速度.

主要方法:

  • 提出了一种神经元修剪技术,该技术在时间域中运行,根据层层明智的膜电压值识别和修剪不那么重要的神经元.
  • 开发了两个修剪值搜索算法,以有效地平衡准确性和计算复杂性,以实现目标计算减小比率.
  • 在 65nm CMOS SNN 处理器中实现了拟议的修剪方案.

主要成果:

  • 实现了57%的能源消耗降低和SNN处理器的2.68倍加速度.
  • 在CIFAR-10数据集上显示的最小准确性损失 (高达0.82%) .
  • 实行了修剪计划,观察到7.3%的面积开销.

结论:

  • 拟议的输入依赖神经元修剪有效地减少了SNN中的计算,从而大大节省能源和更快的处理速度.
  • 这种方法为设计节能SNN处理器提供了可行的策略,而不会大幅降低精度.
  • 时间域修剪方法适用于硬件实现,为实际的神经形态计算铺平了道路.