This paper answers a fundamental question in artificial neural network (ANN)
design: We do not need to build ANNs layer-by-layer sequentially to guarantee
the Directed Acyclic Graph (DAG) property. Drawing inspiration from biological
intelligence (BI), where neurons form a complex, graph-structured network, we
introduce the groundbreaking Cyclic Neural Networks (Cyclic NNs). It emulates
the flexible and dynamic graph nature of biological neural systems, allowing
neuron connections in any graph-like structure, including cycles. This offers
greater adaptability compared to the DAG structure of current ANNs. We further
develop the Graph Over Multi-layer Perceptron, which is the first detailed
model based on this new design paradigm. Experimental validation of the Cyclic
NN's advantages on widely tested datasets in most generalized cases,
demonstrating its superiority over current BP training methods through the use
of a forward-forward (FF) training algorithm. This research illustrates a
totally new ANN design paradigm, which is a significant departure from current
ANN designs, potentially leading to more biologically plausible AI systems.

通过仿生智能 (BI) 中神经元形成复杂的图结构网络的启示，引入了突破性的循环神经网络（Cyclic NNs），模拟生物神经系统灵活和动态的图结构特性，允许神经元之间以包括循环在内的任意图状结构连接，相较于当前 ANN 的有向无环结构，具有更高的适应性。进一步基于这一新设计范式开发了 Graph Over Multi-layer Perceptron，是第一个详细的基于该设计范式的模型。通过在广泛测试数据集上进行实验证实了 Cyclic NN 在大多数常见情况下的优势，并通过使用前向 - 前向（FF）训练算法证明其优于当前 BP 训练方法。本研究展示了一种全新的 ANN 设计范式，与现有 ANN 设计有显著区别，有望引领更符合生物学可行性的人工智能系统。