Categorical compositional distributional semantics is an approach to modelling language that combines the success of vector-based models of meaning with the compositional power of formal semantics. However, this approach was developed without an eye to cognitive plausibility. Vector representations of concepts and concept binding are also of interest in cognitive science, and have been proposed as a way of representing concepts within a biologically plausible spiking neural network. This work proposes a way for compositional distributional semantics to be implemented within a spiking neural network architecture, with the potential to address problems in concept binding, and give a small implementation. We also describe a means of training word representations using labelled images.

翻译：范畴组合分布语义是一种结合了基于向量的意义模型与形式语义组合能力的方法，用于语言建模。然而，这种方法在开发时并未考虑认知合理性。概念的向量表示及其绑定在认知科学中也备受关注，并已被提议作为在生物合理的脉冲神经网络中表示概念的一种方式。本研究提出了一种在脉冲神经网络架构中实现组合分布语义的方法，有望解决概念绑定问题，并给出了一个小型实现方案。我们还描述了一种利用标注图像训练词向量的方法。