混的感知分辨率:可以调整,分歧规范化可以考虑眼际相似性分组和差异增强
Jaelyn R Peiso1, Stephanie E Palmer2, Steven K Shevell3
1University of Chicago, Department of Psychology, Physics Frontier Center for Living Systems, Chicago, IL.
bioRxiv : the preprint server for biology
|April 15, 2024
概括
神经群体竞争解决视觉模两可的问题. 分割性正常化,一种神经计算,解释了大脑如何增强相互竞争的感知之间的相似性或差异,统一相反的感知结果.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 视觉感知 视觉感知 视觉感知
背景情况:
- 视觉系统通常会创造出一个单一的,功能性的世界表现.
- 模两可的视网膜刺激会导致多种解释,引发神经竞争,争夺感知主导地位.
- 语境信息会影响视觉系统如何解决这些模两可的问题.
研究的目的:
- 为了研究底层神经机制的视觉模糊的解决方案.
- 确定分裂性正常化是否可以解释相反的感知结果,例如相似性或差异增强.
- 提出一个统一的计算框架,用于上下文依赖的视觉处理.
主要方法:
- 神经群体动态的计算建模.
- 分析分裂性正常化如何影响神经对模两可的刺激的反应.
- 模拟来证明分裂规范化的能力,以产生分组和差异增强.
主要成果:
- 分数正常化,一种已知的神经计算,可以解释上下文依赖的感官处理.
- 这种计算成功地模拟了对相似性 (分组) 的增强和对竞争感知之间的差异的增强.
- 这些发现为理解看似相反的感知结果提供了一个统一的框架.
结论:
- 分分规范化为解决视觉模糊性提供了统一的计算解释.
- 这种机制解释了大脑如何将类似的感知分组或根据上下文增强差异.
- 这项研究提升了我们对控制感知灵活性和上下文依赖的神经计算的理解.
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