在生物启发的物体识别网络中对物体和场景身份进行编码,以对象和场景身份的稳定性进行编码
Thomas Chapalain1, Bertrand Thirion2, Evelyn Eger3
1Inria, CEA, Université Paris-Saclay, 91120 Palaiseau, France thomas.choche@gmail.com.
Neural computation
|August 14, 2025
概括
训练用于对象识别的深卷积神经网络可以在复杂场景中估计近似数量. 这表明大脑.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 数字感,或在不计数的情况下估计数量,对人类和动物来说至关重要.
- 人工神经网络 (ANN) 显示出作为数字感觉出现的模型的希望.
- 以前的ANN研究使用了简化的刺激,限制了现实世界的适用性.
研究的目的:
- 调查受过对象识别训练的ANN是否能够在复杂的自然场景中感知近似的数量.
- 要确定这个数量信息是否是抽象的,可以从对象属性中分离出来.
- 探索腹部视觉通路在抽象数量表示中的作用.
主要方法:
- 利用新的,合成生成的摄影现实刺激与各种各样的物体和场景.
- 训练深层卷积神经网络 (CNN) 用于对象识别.
- 分析了后来的卷积层中编码的信息,并与未训练的网络进行了比较.
主要成果:
- 训练有素的CNN在各种物体和场景中抽象地编码了近似的数量信息.
- 数量信息可以从以后的卷积层中分布式活动中线性解码.
- 未经训练的网络主要捕获低级特征,未能表示抽象的数字性.
结论:
- 复杂的,自然的刺激对于研究生物和人工系统中的数感至关重要.
- 训练有素的深度CNN为抽象的数字感知提供了一个可行的模型.
- 大脑的腹部视觉通路可能会对抽象的数量表示做出重大贡献.
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