预测性学习规则产生一个像皮层一样的概率感官体验的重播
Toshitake Asabuki1,2,3, Tomoki Fukai1
1Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
eLife
|June 16, 2025
概括
大脑通过重复感官体验来学习,形成内部模型. 这项研究揭示了神经网络如何通过预测性学习和 soma-dendrite 相互作用来实现这一目标,从而影响认知行为.
科学领域:
- 计算神经科学是一种神经科学.
- 认知神经科学 认知神经科学
- 神经网络的神经网络的神经网络
背景情况:
- 大脑构建了环境概率结构的内部模型.
- 自发的大脑活动可能通过重复过去的感官体验来反映这些内部模型.
- 编码这些模型成自发活动的神经机制尚不清楚.
研究的目的:
- 调查反复的神经网络如何学习概率感官体验的自发重复.
- 探索 soma-dendrite 相互作用在神经网络内预测编码中的作用.
- 提出一种用于将内部模型编码为自发大脑活动的新机制.
主要方法:
- 开发了一个循环网络的尖端神经元实施一个预测性学习原则.
- 学习规则将唤起的神经活动与内部驱动的神经活动之间的概率不匹配最小化.
- 分析了网络产生特定刺激的细胞组件和复制行为数据的能力.
主要成果:
- 该模型学会了通过内部驱动的神经动力学来重复概率感官体验.
- 通过反复连接,刺激特异性的细胞组合内部编码了激活概率.
- 该模型的自发活动成功地复制了子感知决策任务中观察到的行为偏见.
结论:
- 具有预测性学习的反复网络可以学习感官体验的自发重复.
- 神经网络中的索马-树突相互作用对于编码内部概率模型至关重要.
- 这些发现强调了细胞内过程和网络动态在认知学习中的重要性.
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