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Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.

作者信息

Plesser Hans Ekkehard

机构信息

Department of Data Science, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.

Institute for Advanced Simulation (IAS-6), Research Centre Jülich, Jülich, Germany.

出版信息

Front Neuroinform. 2024 Oct 23;18:1446620. doi: 10.3389/fninf.2024.1446620. eCollection 2024.

DOI:10.3389/fninf.2024.1446620
PMID:39507425
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11537845/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ed0/11537845/0e9246a53d72/fninf-18-1446620-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ed0/11537845/0e9246a53d72/fninf-18-1446620-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ed0/11537845/0e9246a53d72/fninf-18-1446620-g0001.jpg

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本文引用的文献

1
Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.使用PymoNNto和PymoNNtorch加速脉冲神经网络模拟
Front Neuroinform. 2024 Feb 20;18:1331220. doi: 10.3389/fninf.2024.1331220. eCollection 2024.
2
Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs.使用图形处理器对高度连接的脉冲皮层模型进行快速模拟
Front Comput Neurosci. 2021 Feb 17;15:627620. doi: 10.3389/fncom.2021.627620. eCollection 2021.
3
GPUs Outperform Current HPC and Neuromorphic Solutions in Terms of Speed and Energy When Simulating a Highly-Connected Cortical Model.
在模拟高度连接的皮质模型时，图形处理器（GPU）在速度和能源方面优于当前的高性能计算（HPC）和神经形态解决方案。
Front Neurosci. 2018 Dec 12;12:941. doi: 10.3389/fnins.2018.00941. eCollection 2018.
4
Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model.用于全尺寸皮质微电路模型的数字神经形态硬件SpiNNaker与神经网络模拟软件NEST的性能比较
Front Neurosci. 2018 May 23;12:291. doi: 10.3389/fnins.2018.00291. eCollection 2018.
5
The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model.细胞类型特异性皮质微电路：在全尺度尖峰网络模型中关联结构和活动。
Cereb Cortex. 2014 Mar;24(3):785-806. doi: 10.1093/cercor/bhs358. Epub 2012 Dec 2.
6
Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.离散时间神经网络模拟中具有连续脉冲时间的精确亚阈值积分
Neural Comput. 2007 Jan;19(1):47-79. doi: 10.1162/neco.2007.19.1.47.
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Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.兴奋性和抑制性脉冲发放神经元的稀疏连接网络动力学
J Comput Neurosci. 2000 May-Jun;8(3):183-208. doi: 10.1023/a:1008925309027.
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Exact digital simulation of time-invariant linear systems with applications to neuronal modeling.时不变线性系统的精确数字仿真及其在神经元建模中的应用
Biol Cybern. 1999 Nov;81(5-6):381-402. doi: 10.1007/s004220050570.