层次的VAE提供了灵长类大脑中运动处理的规范性解释
Hadi Vafaii1, Jacob L Yates2, Daniel A Butts1
1University of Maryland, College Park.
bioRxiv : the preprint server for biology
|October 9, 2023
概括
层次的生成模型,如变化自编码器 (VAE),有效地模拟运动感知中的大脑功能. 这些模型通过使用等级推理与神经处理保持一致,改善了对自我运动原因和灵长类神经反应的预测.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习是机器学习.
- 感知和推断的结论.
背景情况:
- 赫尔姆霍尔茨关于感知和推理的19世纪理论反映在现代机器学习生成模型中.
- 变量自编码器 (VAE) 和它们的等级变体为理解这些过程提供了一个计算框架.
结论:
- 层次贝叶斯推理是大脑对世界理解的基础.
- 层次的VAE提供了一个有效的计算模型来模拟基于大脑的感知和推理.
- 神经网络和人工网络之间的调整需要匹配的架构和生态有效的刺激统计数据.
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