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在系统神经科学中的桥梁模型和实验与Cleo:闭环,电生理学和光生理学模拟测试台

Kyle A Johnsen¹, Nathanael A Cruzado², Zachary C Menard¹

¹Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.

bioRxiv : the preprint server for biology

|July 19, 2024

概括

新的Python包Cleo通过将神经群体模型与记录和刺激设备集成来模拟神经科学实验. 这个工具有助于设计更好的实验和验证用于系统神经科学研究的计算模型.

科学领域:

系统神经科学系统神经科学
计算神经科学是一种神经科学.
神经技术的神经技术

背景情况:

系统神经科学的进步为神经活动操纵和记录提供了新的工具.
计算模型越来越多地复制神经活动和行为.
整合这些进步带来了挑战,导致了低于最佳的实验.

研究的目的:

在系统神经科学中弥合计算模型和实验设计之间的差距.
介绍一种用于神经科学实验的新型模拟测试台.
为了促进模型验证和方法工程.

主要方法:

开发了Cleo,这是一个用于模拟神经科学实验的Python包.
综合光遗传学,双光子成像和电极记录模拟.
在Brian spiking神经网络模型中内置了用于闭环控制的现实延迟.

主要成果:

克利奥支持双光子和多光/波长光遗传学,在公共工具中是独一无二的.
该包允许灵活的闭环控制和实验接口的现实模拟.
验证了Cleo的组件和集成的实验,证明了它的实用性.

结论:

克利奥为实验设计,模型验证和方法工程提供了一个低成本的测试平台.
克利奥的开源,模块化性质有助于社区采用和扩展.
克利奥通过实现前性实验模拟来推进光遗传技术.

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