具有自上而下的抑制反的神经电路在多感官集成中优于最佳贝叶斯集成
Yelin Dong1,2, Hongzhi You3, Yuxiu Shao4
1School of Systems Science and State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Neuroscience bulletin
|November 9, 2025
概括
循环神经网络可以解释大脑如何整合多种感官,表明反预测可以改善或阻碍最佳贝叶斯整合 (OBI) 性能. 这挑战了独立感官处理的观点.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 神经科学是一个神经科学.
背景情况:
- 贝叶斯集成是多感官集成的一个关键理论.
- 人们认为Feedforward神经网络可以建模最佳贝叶斯集成 (OBI).
- 神经反预测很常见,但它们在多感官OBI中的作用尚不清楚.
研究的目的:
- 研究反预测的循环神经网络如何为多感官最佳贝叶斯集成 (OBI) 贡献.
- 探索feedforward-feedback相互作用对多感官集成性能的影响.
- 了解非线性神经元相互作用在调解整合行为中的作用.
主要方法:
- 模拟了一个双层神经电路模型与相互投影.
- 该模型使用单或双感官模式执行感知歧视任务.
- 分析了与OBI相对的模型性能,在不同的feedforward-feedback相互作用下.
主要成果:
- 循环模型可以与OBI相匹配,表现不佳或表现优于OBI,这取决于feedforward-feedback平衡.
- 模型性能可变性与现有的实验发现保持一致.
- 神经元组合中的非线性相互作用对于调解集成行为至关重要.
结论:
- 循环神经网络为理解多感官集成提供了一个可行的框架.
- 上下反可以纠感官模式,挑战独立的传统观点.
- 反和前预测之间的相互作用解释了对OBI的偏差.
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