The core purpose of developing artificial neural networks was to mimic the functionalities of biological neural networks. However, unlike biological neural networks, traditional artificial neural networks are often structured hierarchically, which can impede the flow of information between neurons as the neurons in the same layer have no connections between them. Hence, we propose a more robust model of artificial neural networks where the hidden neurons, residing in the same hidden layer, are interconnected, enabling the neurons to learn complex patterns and speeding up the convergence rate. With the experimental study of our proposed model as fully connected layers in shallow and deep networks, we demonstrate that the model results in a significant increase in convergence rate.

翻译：开发人工神经网络的核心目的是模拟生物神经网络的功能。然而，与生物神经网络不同，传统人工神经网络通常采用分层结构，这种结构可能阻碍神经元间的信息流动，因为同一层的神经元之间不存在连接。为此，我们提出一种更鲁棒的人工神经网络模型，其中位于同一隐藏层的隐藏神经元相互连接，使神经元能够学习复杂模式并加快收敛速度。通过对所提模型在浅层和深层网络中作为全连接层的实验研究，我们证明该模型显著提升了收敛速度。