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相关概念视频

Neural Circuits01:25

Neural Circuits

964
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...
964
Signal and System01:26

Signal and System

605
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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相关实验视频

Updated: May 22, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

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基于三角尖的神经形态信号处理系统.

Shuai Wang1, Dehao Zhang1, Ammar Belatreche2

  • 1Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural networks : the official journal of the International Neural Network Society
|March 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的神经形态信号处理系统,使用尖端神经网络 (SNN) 和量子化. 该系统实现了最先进的性能,显著降低了内存和能源消耗,以高效地部署边缘设备.

关键词:
关键字发现和EEG.神经对信号进行编码.神经信号处理神经信号处理.量子化激增神经网络的神经网络.三级尖端神经网络的神经网络.

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Construction of Microdrive Arrays for Chronic Neural Recordings in Awake Behaving Mice
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Construction of Microdrive Arrays for Chronic Neural Recordings in Awake Behaving Mice

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相关实验视频

Last Updated: May 22, 2025

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Interfacing Microfluidics with Microelectrode Arrays for Studying Neuronal Communication and Axonal Signal Propagation
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科学领域:

  • 神经形态工程的神经形态工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 深度神经网络 (DNN) 在信号处理方面提供了高性能,但需要大量的计算资源,限制了边缘设备的应用.
  • 边缘设备的资源限制需要节能和轻量化处理解决方案.

研究的目的:

  • 为资源有限的边缘设备开发一种节能,轻量级的神经形态信号处理系统.
  • 为了提高效率,利用尖端神经网络 (SNN) 和量子化技术.

主要方法:

  • 开发了一种值适应编码 (TAE) 方法,将模拟信号转换为稀疏的三元尖峰列车,减少能量和内存.
  • 引入了与TAE兼容的量化三元SNN (QT-SNN),量化膜潜力和突触重量以减少记忆.
  • 评估了系统的语音和脑电图 (EEG) 识别任务.

主要成果:

  • 在信号处理任务中实现了最先进的 (SOTA) 性能.
  • 与现有方法相比,显示了94%的内存需求减少.
  • 通过理论分析展示了比其他SNN方法大7.5倍的节能效果.

结论:

  • 拟议的神经形态系统为边缘设备的信号处理提供了高效和有效的解决方案.
  • TAE和QT-SNN的组合显著降低了内存和能量需求,同时保持了高性能.
  • 这项工作为在现实世界应用中节能信号处理提供了一个有前途的方向.