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.

In this project, I've built a neural network architecture with a static execution graph that acts as a dynamic neural network in which connections between various neurons are controlled by the network itself. This is accomplished by manipulating the adjacency matrix representation of the network on a per-neuron basis with cell elements representing a'distance', and masking off connections that are within a threshold. Including a loss term based on the networks sparsity or processing time allows the architecture to optimize its structure for accuracy or speed. Alright, so hopefully I've caught your attention with the title. To begin, I'd like to explain a little behind why I've created this. My educational background is actually in the sciences, just at the junction between chemistry and physics.