小相关扩展用于量化噪音感应系统中的信息
Gabriel Mahuas1,2, Olivier Marre1, Thierry Mora2
1Institut de la Vision, Sorbonne Université, CNRS, INSERM, 17 rue Moreau, 75012 Paris, France.
Physical review. E
|September 19, 2023
概括
神经网络通过集体神经元活动传输信息,但相关性使分析复杂化. 这项研究引入了一种新的方法来计算大神经群中的信息,揭示了对噪音和记忆效应的洞察力.
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:
- 神经网络通过集体尖端活动编码刺激.
- 人口反应显示出噪音和复杂的相关性,阻碍了信息传输分析.
- 由于尺寸性挑战,现有的方法仅限于小的神经元组.
研究的目的:
- 开发一种可扩展的方法来计算大神经群体中的刺激信息.
- 分析性地描述神经元相关性对信息编码的影响.
- 将该方法应用于现实世界的神经数据,以了解噪音和记忆效应.
主要方法:
- 开发了一个小相关性扩张近似.
- 基于发射率和对对相关性的刺激信息的衍生分析表达式.
- 使用合成数据和来自脊椎动物视网膜的电生理学记录验证了近似值.
主要成果:
- 小相关性扩展在大神经群体中准确计算刺激信息.
- 该方法提供可解释的分析表达式.
- 量化了噪声相关性和单个神经元记忆对视网膜数据中信息传输的影响.
结论:
- 开发的近似方法克服了在大型神经群体中分析信息的维度的诅咒.
- 这种方法为剖析神经编码中相关性和单个神经元属性的作用提供了一个强大的工具.
- 这些发现对理解视网膜等感官系统中的神经计算有意义.
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