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相关概念视频

Network Function of a Circuit01:25

Network Function of a Circuit

254
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
254

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相关实验视频

Updated: May 27, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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在模拟神经形态硬件上进行可扩展的网络仿真.

Elias Arnold1, Philipp Spilger1, Jan V Straub1

  • 1European Institute for Neuromorphic Computing, Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, Germany.

Frontiers in neuroscience
|February 20, 2025
PubMed
概括

一个新的软件功能可以在BrainScaleS-2神经形态系统上对大型尖端神经网络进行分区模拟. 这允许训练比硬件物理支持的更大的深度神经网络,推进神经形态计算.

关键词:
加速器抽象的抽象建模 建模模型 建模模型这是一个神经形态神经形态的神经形态.刺激神经网络的神经网络.虚拟化是一种虚拟化.

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相关实验视频

Last Updated: May 27, 2025

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11:18

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科学领域:

  • 神经形态工程的神经形态工程
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 该BrainScaleS-2平台提供加速的神经形态计算.
  • 模拟大规模尖端神经网络 (SNN) 面临硬件大小限制.
  • 深度SNN对于高级AI任务至关重要.

研究的目的:

  • 在BrainScaleS-2.0上引入一个用于分区模拟大规模SNN的软件功能.
  • 为了使超越单芯片物理限制的SNNs的培训.
  • 为了促进缩放的神经形态系统的性能评估.

主要方法:

  • 开发了一个用于分区SNN模拟的新型软件功能.
  • 在规模不足的神经形态资源上实现了序列模型模拟.
  • 在超过BrainScaleS-2大小的MNIST和EuroSAT数据集上训练深度SNN模型.

主要成果:

  • 成功证明了大规模SNNs的分区模拟.
  • 训练有素的深度SNN模型大于物理BrainScaleS-2基板.
  • 使用MNIST和EuroSAT数据集验证了该方法.

结论:

  • 软件功能可以模拟和训练比物理硬件更大的SNN.
  • 这有助于对未来规模化的神经形态系统进行准确的性能评估.
  • 推动大规模SNNs和神经形态计算的开发和理解.