Dendritic Learning-Based DenseNet for Classification

Dendritic neurons play a crucial role in information processing in neural circuits. Inspired by these neurons, researchers have developed dendritic neural models (DNM) that integrate their properties into conventional deep learning models, yielding outstanding results in various tasks. In this study, DDenseNet model is proposed. DDenseNet combines the advantages of DenseNet and DNM to better simulate brain neuron characteristics and improve deep learning model performance. Layer Normalization (LayerNorm) is added to our model to stabilize data feature distributions and increase convergence speed. Experimental results show that DDenseNet outperforms traditional DenseNet and even some established classic deep learning models in classification tasks. The study suggests that using DNM as a classifier has the potential to create more efficient deep learning models for classification tasks.