在卷积神经网络中介于隐蔽注意的新兴神经机制
Sudhanshu Srivastava1,2, William Yang Wang2,3,4, Miguel P Eckstein1,2,3,4,5
1Graduate Program in Dynamical Neuroscience, University of California Santa Barbara, Santa Barbara, CA 93106.
概括
卷积神经网络 (CNN) 显示出新兴的隐蔽注意力机制,在没有明确编程的情况下反映大脑功能. 这项研究揭示了新的神经元细胞类型和潜在的注意力的计算策略.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 视觉感知 视觉感知 视觉感知
背景情况:
- 隐藏的视觉注意力使选择性信息处理能够在没有眼动的情况下进行.
- 预测线索通过引导注意力来增强感知.
- 现有的计算模型往往明确包含注意力机制.
研究的目的:
- 为了研究表现出隐蔽注意力的卷积神经网络 (CNN) 的新兴神经机制.
- 将CNN的注意力机制与生物系统和贝叶斯理想观察者进行比较.
- 在CNN和生物系统中识别新型神经元类型和计算策略.
主要方法:
- 在CNN中分析了180万个单元,这些单元是根据使用神经科学启发的方法在提示范式上训练的.
- 将CNN计算阶段与贝叶斯理想观察者 (BIO) 模型进行比较.
- 在提示任务期间,重新分析来自小鼠上层结合体的神经元活动.
主要成果:
- 在早期层中,CNN表现出视网膜类神经元,并在后期层中表现出关节调,反映神经生理学.
- CNNs使用类似于BIO的总和和新的机制,如对立性和修正线性单元相互作用.
- 美国有线电视新闻网的分析预测并确定了小鼠中的提示抑制,位置总和和位置对手细胞.
结论:
- 在没有明确的编程的情况下,CNN开发了新兴的秘密注意力机制,提供了对生物系统的洞察力.
- 这项研究确定了新的计算机制和神经元类型,这些新机制和神经元类型有助于隐藏注意.
- 这种方法为特征注意力和发现新的神经机制提供了一个框架.
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