Associative Learning using a Deep neural network based on unsupervised representation

学习是我们获取周围世界知识的方式，正是通过这一获取知识的过程，环境提醒我们做出行为反应。知识的获取是一个基于我们感知的积极的、持续的认知过程。在大脑中，知识是通过联想不同的感觉数据类型来学习的，比如图像和声音。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。联想记忆模型以前被用来模拟这样的学习过程[3]，但这些更简单的架构无法处理大型复杂数据。联想记忆的概念与刺激联想之间的过程吻合得很好。经历共现刺激，即图1，雷电形成两种联系。这些刺激相互连接，形成了照明自动联想（AA）的表征。大脑还学会了另一种联想——雷电异联想（HA）。心理学理论对这两种过程进行了区分和排序，知觉机制形成了表征。接下来是预测学习的过程，对这些表征进行操作。

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.