A dynamical-structure neural network model specified for representing logical relations with inhibitory links and fewer neurons
یک مدل شبکه عصبی با ساختار دینامیکی برای نشان دادن روابط منطقی با پیوندهای بازدارنده و سلولهای عصبی کمتری مشخص شده است-2020
To make ANNs have the ability of logical processing in order to fulfil the urgent requirement that com- puters can automatically judge according to numerous specific conditions, researches have been carried out to design novel neural network models for representing logical relations. Recently, a new ANN model for representing logical relations is proposed. In the model, six components are designed to simulate the operations of logic gates. The work provides a novel way for constructing logical relations running in a neural-like manner. However, the components are still complex and indirect for the representation since more extra neurons and links are needed to simulate logic gates. In order to represent logical relations more directly, this paper defines new neurons and multiple kinds of links to represent logic gates directly, and they can be combined to represent complex logical relations in a simpler neural network structures with fewer neurons. Additionally, this ANN model can dynamically create links on demand instead of the fixed full connections. It can constantly adjust its network structure when getting the data continuously. It can be used for the establishment of the rule library of the intelligent information system in the form of the neural network structure.
Keywords: Brain-inspired computing | Logical representation | Dynamical neural network structure | Inhibitory link | Adaptivity | Rule library