学习从外部引起的光流通过传感器电机不匹配电路进行自我生成的细分
Matthias Brucklacher1, Giovanni Pezzulo2, Francesco Mannella2
1Cognitive and Systems Neuroscience, University of Amsterdam, 1098XH Amsterdam, Netherlands.
概括
这项研究引入了一个神经网络模型,可以区分自我生成的光流与外部运动,改善视觉感知和对象分类. 它扩展了活动剂学习运动后果的预测编码.
科学领域:
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
- 视觉处理 视觉处理
背景情况:
- 有效的感官检测需要过不相关的信息,例如区分自动运动光流与与物体相关的视觉输入.
- 带有感应运动不匹配检测的预测编码模型为理解这一过程提供了一个框架,在早期视觉区域有证据.
- 将这些不匹配信号集成到皮质网络中进行细分和分类仍然是一个悬而未决的问题.
研究的目的:
- 开发一个生物可信的计算模型,扩展预测编码,以区分自我生成和外部引起的光流.
- 调查传感运动不匹配信号如何在神经网络中集成,用于视觉输入细分和对象分类.
- 阐明反连接在维护光流处理的生成模型中的作用.
主要方法:
- 开发了一个三神经元微电路来建模经验依赖的感觉运动不匹配反应,与小鼠图像数据进行验证.
- 这个微电路被集成到一个更大的神经网络中,其中有两个生成流:一个用于自动运动预测,另一个用于外部光流模型.
- 该模型结合了运动选择性更高视觉区域 (mHVA) 和V1之间的双向连接,强调用于生成模型维护的反连接.
主要成果:
- 拟议的三神经元电路成功地复制了实验数据中观察到的感觉运动不匹配反应.
- 双流神经网络展示了区分自我生成和外部引起的光流的能力.
- 该模型表明,mHVA学会对移动物体进行细分,并促进分类,其架构映射到灵长类视觉皮层.
结论:
- 该模型提供了一个生物学上可信的机制,用于在视觉系统内将自我运动与外部视觉刺激分开.
- 它将Hebbian预测编码原理扩展到感觉运动环境中,使代理商能够学习和预测他们自己的运动的感觉后果.
- 这些发现强调了在更高的视觉区域中反连接的关键作用,以维持视觉感知和分类所必需的生成模型.
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