One of the claims of Type Theory with Records is that it can be used to model types learned by agents in order to classify objects and events in the world, including speech events. That is, the types can be represented by patterns of neural activation in the brain. This claim would be empty if it turns out that the types are in principle impossible to represent on a finite network of neurons. We will discuss how to represent types in terms of neural events on a network and present a preliminary computational implementation that maps types to events on a network. The kind of networks we will use are closely related to the transparent neural networks discussed by Strannegård.

中文翻译：

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将类型表示为神经事件

Type Theory with Records 的主张之一是它可用于对代理学习的类型进行建模，以便对世界上的对象和事件（包括语音事件）进行分类。也就是说，这些类型可以用大脑中的神经激活模式来表示。如果事实证明这些类型原则上不可能在有限的神经元网络上表示，那么这种说法将是空洞的。我们将讨论如何根据网络上的神经事件来表示类型，并展示将类型映射到网络上的事件的初步计算实现。我们将使用的网络类型与 Strannegård 讨论的透明神经网络密切相关。