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Neural Circuits

Neural Circuits

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...

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Updated: Jul 20, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

边缘地图:在边缘计算中用于尖端神经网络的优化映射工具链.

Jianwei Xue¹, Lisheng Xie¹, Faquan Chen¹

¹School of Electronic and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)

|July 29, 2023

概括

EdgeMap优化了边缘设备的尖端神经网络 (SNN),大大降低了延迟,能源使用和通信成本. 这个工具链增强了SNN在神经形态硬件上的部署,以实现高效的边缘计算应用程序.

关键词:

边缘计算是一种边缘计算.绘制地图,绘制地图.神经形态硬件的神经形态硬件刺激神经网络的神经网络.

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

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Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

相关实验视频

Last Updated: Jul 20, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Published on: March 25, 2014

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

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Published on: March 2, 2015

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

科学领域:

人工智能的人工智能
神经形态计算是一种神经形态计算.
边缘计算边缘计算

背景情况:

尖端神经网络 (SNN) 为边缘计算提供智能和能源效率.
目前在神经形态硬件上的SNN映射方法存在高延迟,低吞吐量和糟糕的能源/连接管理问题.
这些局限性阻碍了SNN在边缘计算中的实际部署.

研究的目的:

介绍EdgeMap,这是一个优化的工具链,用于在边缘设备上部署SNN.
克服现有的SNN映射方案的性能和效率限制.
在边缘计算场景中实现高性能,高能效的SNN应用.

主要方法:

使用流式图表分区算法进行SNN图形分区,根据硬件约束对神经元进行聚类.
多目标优化算法,在绘图过程中最大限度地降低能源和通信成本.
在四个不同的SNN应用程序中对EdgeMap的评估.

主要成果:

EdgeMap的平均延迟时间降低了高达19.8%,能源消耗降低了57%,通信成本降低了58%.
执行时间被提高了1225.44×的因素.
与最先进的方法相比,吞吐量增加了高达4.02×.

结论:

EdgeMap提供了一个高效和有效的解决方案,用于在边缘设备上部署SNNs.
工具链显著提高了对边缘计算至关重要的性能指标.
在资源受限的边缘环境中,EdgeMap证明了对现实世界SNN应用程序的强大实用性.