This paper introduces a new neural network model that aims to mimic the biological brain more closely by structuring the network as a complete directed graph that processes continuous data for each timestep. Current neural networks have structures that vaguely mimic the brain structure, such as neurons, convolutions, and recurrence. The model proposed in this paper adds additional structural properties by introducing cycles into the neuron connections and removing the sequential nature commonly seen in other network layers. Furthermore, the model has continuous input and output, inspired by spiking neural networks, which allows the network to learn a process of classification, rather than simply returning the final result.

翻译：本文提出一种新型神经网络模型，通过将网络构建为在每个时间步处理连续数据的完全有向图，更紧密地模拟生物脑结构。当前神经网络在结构上对脑组织进行了粗略仿生（如神经元、卷积和循环机制）。本文提出的模型通过引入神经元连接中的循环结构并消除其他网络层常见的序列化特性，增加了额外结构属性。此外，受脉冲神经网络启发，该模型采用连续输入输出机制，使网络能够学习分类过程而非简单返回最终结果。