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信息几何学的应用到神经网络活动的激增神经网络活动

Jacob T Crosser¹, Braden A W Brinkman¹

¹Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA.

Physical review. E

|March 16, 2024

概括

研究人员使用信息几何学来探索神经网络的行为,揭示了激发-抑制平衡如何影响网络对特定统计参数的敏感性.

科学领域:

计算神经科学是一种神经科学.
系统神经科学系统神经科学
信息理论信息理论

背景情况:

复杂的生物系统,比如大脑,表现出巨大的行为空间,通常由于环境噪音或内部混乱而表现为随机.
了解神经回路如何利用特定的尖峰模式,以及这些如何随电路特性而改变,是神经科学的一个关键挑战.
信息几何学提供了工具来量化概率模型及其参数之间的关系.

研究的目的:

将信息几何应用到神经网络模型中,以了解响应时间尺度对网络信息几何的影响.
识别模型行为至关重要的统计参数,以及它们的重要性如何随网络属性而变化.

主要方法:

利用信息几何学来研究概率模型嵌入到多元体中的情况.
应用超标嵌入到激发性和抑制性神经群体的网络模型.
分析了膜和突触响应时间尺度之间的竞争如何塑造网络的信息几何.

主要成果:

超标嵌入成功识别了影响模型行为的关键统计参数.
证明了这些敏感参数的排名变化基于激发和抑制在网络中的平衡.
揭示了响应时间尺度如何相互作用来塑造网络的信息几何.

结论:

信息几何学为分析复杂神经系统的行为提供了一个强大的框架.
激发和抑制的平衡严重改变神经网络模型对特定参数的敏感性.
这种方法在数量上将神经电路属性与网络的新兴计算作品集联系起来.

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