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SNS-Toolbox:一个开源工具,用于设计合成神经系统,并将其与网络物理系统连接起来
William R P Nourse1, Clayton Jackson2, Nicholas S Szczecinski3
1Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Biomimetics (Basel, Switzerland)
|June 27, 2023
概括
我们介绍SNS-Toolbox,这是一个开源的Python包,用于实时模拟数千个神经元的合成神经系统 (SNS). 该工具通过弥合简化和详细的神经模型之间的差距来促进先进的机器人控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 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:
- 合成神经系统 (SNS) 为使用动态神经元和基于导电性的突触进行机器人控制提供了一个有希望的方法.
- 现有的神经模拟软件与SNS网络中常见的复杂拓和异质神经元类型作斗争.
- 目前的工具适用于小的,详细的模型或大规模的简化模型,留下中间复杂性的差距.
研究的目的:
- 介绍SNS-Toolbox,一个开源的Python包,旨在模拟合成神经系统.
- 为了实时或更快地模拟数百到数千个尖端和非尖端神经元.
- 为了降低在机器人控制应用中设计和实施SNS网络的进入障碍.
主要方法:
- 开发了SNS-Toolbox作为一个开源的Python包.
- 实现了用于模拟基于导电性的突触的尖端和非尖端神经元的支持.
- 评估了各种硬件后端的性能,包括GPU和嵌入式系统.
- 集成的SNS-工具箱与物理模拟器 (Mujoco) 和机器人中间件 (ROS).
主要成果:
- 对于异质神经元的大型网络,SNS-Toolbox可以实现实时或更快的模拟速度.
- 该软件支持各种神经和突触模型,容纳复杂的SNS设计.
- 在控制模拟肢体和移动机器人方面证明了成功的应用.
结论:
- 在规模上,SNS-Toolbox有效地模拟复杂的合成神经系统.
- 该包增强了使用SNS用于高级机器人控制的可行性.
- 这种开源工具预计将增加SNS在机器人研究和开发中的应用.
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