从训练有素的尖端反复的神经网络中,从状态空间几何学中出现的感知偏差
Luis Serrano-Fernández1, Manuel Beirán2, Néstor Parga1
1Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain; Centro de Investigación Avanzada en Física Fundamental, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Cell reports
|July 5, 2024
概括
工作记忆导致刺激感知转向平均水平. 训练分辨间隔的神经网络表现出这种偏差,在感官维护过程中揭示贝叶斯计算.
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.
- 认知科学 认知科学
背景情况:
- 在工作记忆中保存的刺激的感知受到收缩偏差,在这种偏差中,刺激被认为更接近平均值.
- 虽然这种心理物理现象得到了充分的证据,但它的神经基础在很大程度上仍未被探索.
研究的目的:
- 研究工作记忆中的收缩偏差的神经机制和起源.
- 探索神经人口活动和行为偏见之间的关系.
主要方法:
- 训练尖端神经元的反复网络来执行时间间隔区分任务.
- 在状态空间中分析神经轨迹的几何特征.
- 开发一种规范模型来解释神经表征.
主要成果:
- 训练有素的神经网络表现出与动物观察到的收缩偏差一致的行为.
- 神经轨迹编码了刺激持续时间的扭曲表示,由感官历史调节.
- 这些表示被证明反映了贝叶斯对间隔持续时间的估计.
结论:
- 贝叶斯计算甚至在最初的感官阶段参与处理感官信息.
- 这些计算在整个工作记忆中保持刺激的过程中都会持续下去.
- 工作记忆中的神经活动与行为偏见直接相关并解释.
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