预期的中心周围抑制及其潜在的计算和人工神经网络模型
Ling Huang1,2, Shiqi Shen1,2, Yueling Sun1,2
1Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China.
eLife
|November 28, 2025
概括
预期通过增强预期的刺激和抑制类似的无关紧要的刺激来提高感知. 这种预测编码机制优化了我们如何解释适应性行为的感官信息.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 预期有助于通过快速解释感官信息来帮助适应性行为.
- 基于预期的处理的神经机制和精确的概况仍然不完全理解.
研究的目的:
- 阐明预期对感知影响的基础神经计算.
- 在定向处理中研究期望的空间概况.
- 确定预测编码在预期驱动的感知调制中的作用.
主要方法:
- 心理物理实验测量导向歧视和调整.
- 计算机建模模拟神经反应.
- 在卷积神经网络 (CNN) 中进行除研究,以测试预测编码假设.
主要成果:
- 在导向空间中观察到一个中心周围抑制配置,当参与者期望特定的格子导向时.
- 这种抑制特征独立于注意力任务的相关性.
- 计算模型支持两个机制:预期的方向调整曲线的化或意想不到的曲线的转移.
- 在CNN中预测编码反对于复制观察到的中心-周围抑制至关重要.
结论:
- 期望通过增强和抑制来动态调节感知.
- 这种双重机制通过优先考虑预期的表示和减弱干扰的表示来优化解释.
- 预测编码是一种关键的神经计算,是预期感知效应的基础.
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