Despite artificial neural networks being inspired by the functionalities of biological neural networks, unlike biological neural networks, conventional 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 that leads to rapid convergence. With the experimental study of our proposed model in deep networks, we demonstrate that the model results in a noticeable increase in convergence rate compared to the conventional feed-forward neural network.

翻译：尽管人工神经网络灵感来源于生物神经网络的功能，但与生物神经网络不同，传统人工神经网络通常采用层次化结构，同一层内的神经元之间缺乏连接，这可能阻碍神经元之间的信息流动。为此，我们提出了一种更鲁棒的人工神经网络模型，其中同一隐藏层内的隐藏神经元相互连接，从而实现快速收敛。通过对我们提出的模型在深度网络中的实验研究，我们证明该模型相比传统前馈神经网络，在收敛速度上具有显著提升。