关于标准和受大脑限制的深度神经网络支持认知叠加的能力:一个立场论文
1Department of Computing, Goldsmiths - University of London, London, UK.
Cognitive neurodynamics
|December 23, 2024
概括
由大脑启发的神经网络自然支持叠加,即结合多个概念的能力. 标准的深度神经网络 (DNN) 难以实现这一对一般智能的关键功能.
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.
- 认知科学是一种认知科学.
背景情况:
- 共同激活多个概念 (叠加) 对复杂的认知至关重要.
- 这种能力对于人工通用智能 (AGI) 是至关重要的.
- 标准的前深度神经网络 (DNN) 缺乏这种能力.
研究的目的:
- 为了研究大脑受约束的神经网络对叠加的能力.
- 将它们的功能与标准DNN进行对比.
- 解释神经架构中叠加的基本机制.
主要方法:
- 关于深层,类似大脑的神经网络的新型模拟.
- 培训网络与生物现实的Hebbian学习.
- 分析出现的内部电路 (细胞组合).
主要成果:
- 类似大脑的网络自发地发展出能够叠加的细胞组.
- 这些网络利用神经生理学上准确的学习和现实的连接.
- 经过梯度下降训练的DNN未能表现出叠加.
结论:
- 由于现实的学习和连接性,深度,大脑受约束的架构自然支持叠加.
- 标准DNN不适合叠加,这是一个关键的认知操作.
- 这凸显了类似大脑的人工智能与当前用于AGI开发的DNN之间的差异.
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