模拟分裂性规范化对噪声相关性的多种影响
Oren Weiss1, Hayley A Bounds2, Hillel Adesnik2,3
1Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America.
PLoS computational biology
|November 30, 2023
概括
这项研究引入了神经反应的新模型,揭示了分裂性正常化如何影响神经元之间的噪声相关性. 该模型准确地描述神经数据,并建议在视觉皮层中共享正常化信号.
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
- 神经编码 神经编码
背景情况:
- 分割性正常化是大脑区域中神经活动的关键模型.
- 它对人口层面神经反应统计数据的影响,如噪音相关性,尚未得到充分研究.
- 现有的神经共变性模型往往忽视了正常化的影响.
研究的目的:
- 开发一种双向随机分裂正常化模型,以解释神经响应的共变性.
- 调查正常化如何影响噪声相关性,考虑共享与非共享的正常化信号.
- 将模型应用于经验数据,并评估其性能与替代方案相比.
主要方法:
- 开发了一种双向随机分离的正常化模型.
- 对正常化如何调节噪声相关性的理论分析.
- 使用小鼠初级视觉皮层 (V1) 的成像数据来应用和验证模型 (V1).
主要成果:
- 拟议的模型准确地适应V1成像数据,往往优于其他相关性模型.
- 正常化对噪声相关性的影响取决于正常化信号是否在神经元之间共享.
- 分析表明,正常化信号经常在研究数据集中的V1神经元之间共享.
结论:
- 开发的模型为量化正常化和神经共变性之间的关系提供了一个框架.
- 这项工作为规范化背后的电路机制提供了新的见解.
- 这些发现突出了正常化在神经信息处理和表示中的作用.
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