Associative Learning using a Deep neural network based on unsupervised representation

使用基于无监督表示的深度神经网络进行联想学习

• 批准号: 2578604
• 金额: --
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2578604
• 关键词: Associative; Learning; using; Deep; neural

Learning is the way we acquire knowledge about the world around us, and it is through this process of knowledge acquisition, that the environment alerts our behavioural responses. The acquisition of knowledge is an active and ongoing cognitive process based on our perceptions. In the brain, knowledge is learnt by associating different sensory data types, such as image and voice.Associative memory plays a crucial role in our ability to link things together and forms a large part of human intelligence. Associative memory models have previously been used to imitate such a learning process [3], but these simpler architectures have failed to deal with large complex data. The concept of associative memories matches well with the processes between stimulus associations. Experiencing co-occurrent stimuli, i.e. Figure 1 lighting and thunder forms two kinds of links. These stimuli connect and form a representation of the lighting auto-associative (AA). The mind also learns another association lighting-thunder Hetero-associative (HA).Psychology theory distinguishes between these two processes and assigns them an order, Perceptual mechanisms form the representation. Process of predictive learning follows, operating over those representations.

学习是我们获取关于周围世界的知识的方式，正是通过这种知识获取过程，环境提醒我们的行为反应。知识的获得是一个基于我们的感知的主动和持续的认知过程。在大脑中，知识是通过关联不同的感觉数据类型（如图像和声音）来学习的。联想记忆在我们将事物联系在一起的能力中起着至关重要的作用，并构成了人类智力的很大一部分。联想记忆模型以前曾被用来模拟这样的学习过程[3]，但这些更简单的架构未能处理大型复杂数据。联想记忆的概念与刺激联想之间的过程相匹配。经历共时刺激，即图1闪电和雷声形成两种联系。这些刺激连接并形成照明自联想（AA）的表示。心理学理论区分了这两个过程，并给它们分配了一个顺序，感知机制形成了表征。预测学习的过程如下，在这些表示上操作。