混乱的神经动力学通过采样促进了概率计算
Yu Terada1,2,3, Taro Toyoizumi1,4
1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama 351-0198, Japan.
概括
混乱的神经动力学,通过突触可塑性学习,使循环神经网络能够进行感官集成. 这种混乱的活动模型将大脑功能作为贝叶斯生成模型,解释神经变异性.
科学领域:
- 计算神经科学是一种计算神经科学.
- 神经动力学 神经动力学
- 贝叶斯的推理 贝叶斯的推理
背景情况:
- 皮层神经元在试验和时间之间显示出显著的响应变化.
- 这种变化在理论上与反复出现的神经网络中的混乱动态有关.
- 了解这种变化的计算基础对于神经科学至关重要.
研究的目的:
- 为了证明由突触学习诱导的混乱的神经动态,促进感官暗示集成.
- 探索这些动态如何支持对静态和动态变量的采样计算.
- 调查自发活动在表示 priors 和计算边际分布中的作用.
主要方法:
- 利用具有生物可信的突触学习规则的循环神经网络.
- 模拟网络动态,观察出现的混乱行为.
- 评估网络执行感官暗示集成和推断任务的能力.
- 分析了代表性内容的自发活动.
主要成果:
- 新兴的混乱动态通过突触学习成功诱导.
- 网络展示了有效的感官暗示集成,使用采样方法.
- 混乱动态使得静态变量和动态变量的样本生成成为可能.
- 网络将学习的刺激唤起的样本用于推断,即使有不完整的感官信息.
结论:
- 混乱的神经动态为基于采样的感官集成和推理提供了基质.
- 学习的混乱动态可以在神经网络中实现贝叶斯生成模型.
- 混乱网络中的自发活动可能代表先验,并促进边际分布的计算.
- 这项工作为理解神经变化和大脑功能提供了一个计算框架.
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