预测错误的局部最小化驱动学习不变的对象表示在视觉感知生成网络模型的学习
Matthias Brucklacher1, Sander M Bohté1,2, Jorge F Mejias1
1Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.
Frontiers in computational neuroscience
|October 11, 2023
概括
一个新的预测编码网络使用单一规则学习对象识别和生成. 这种生物学上可信的模型实现了不变的对象表示,并重建图像,即使缺少信息.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 腹部视觉流需要不变物体识别和生成建模来完成诸如填写隐藏信息之类的任务.
- 现有的模型往往难以以生物学上合理地整合这些功能.
研究的目的:
- 为了证明多层预测编码网络如何能够学习对象识别和生成.
- 为了研究一个生物可信的学习规则不变的视觉表示.
主要方法:
- 开发了一种多层预测编码网络,该网络以连续转换的对象的序列进行训练.
- 采用单一的学习规则:局部最小化预测错误.
- 将训练范式转变为动态输入.
主要成果:
- 在最高网络区域的神经元实现了对象身份不变性,类似于灵长类动物内神经元.
- 该网络在腹部处理流中复制了实验观察到的时间尺度.
- 通过重建封闭物体图像来证明产生能力.
结论:
- 预测编码网络通过统一的,生物可信的机制成功地学习不变的对象识别和图像生成.
- 该模型将预测编码框架推广到动态输入,提供比非本地错误反传播方法更现实的方法.
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