在训练识别视觉对象的深度神经网络中,情绪选择性的出现
Peng Liu1,2, Ke Bo2, Mingzhou Ding1
1J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America.
PLoS computational biology
|March 28, 2024
概括
卷积神经网络 (CNN) 显示,视觉皮层可以内在地代表视觉信息的情感意义. 在CNN模型中操纵人工神经元改变了情绪识别,支持了先天的视觉处理能力.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
背景情况:
- 神经成像研究表明,视觉皮质对于处理视觉刺激的情感意义至关重要.
- 一个关键的辩论是,这些影响特定的视觉表现是视觉系统内在的,还是来自像杏仁体这样的情感中心的反.
研究的目的:
- 研究视觉皮层中情感特异视觉表现的起源.
- 用计算模型测试视觉系统本质上代表情感意义的假设.
主要方法:
- 利用卷积神经网络 (CNN) 模型模拟人类腹部视觉皮层,在ImageNet上进行预训练.
- 采用了两个情感图像数据集来调查CNNs.
- 在对情感刺激有选择性的人工神经元上进行了损伤 (降低输出) 和增强 (增加收益) 实验.
主要成果:
- 在所有CNN层中确定了选择性地对中性,愉快或不愉快图像做出反应的人工神经元.
- 损伤这些神经元会影响情绪识别性能.
- 增强这些神经元可以提高情绪识别性能.
结论:
- 这些发现支持了视觉系统具有表达视觉输入情感意义的内在能力的理论.
- 卷积神经网络为测试有关视觉处理和情感的神经科学理论提供了一个有价值的计算平台.
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