Dendritic SE-ResNet Learning for Bioinformatic Classification

The construction of neural networks is a widely adopted approach in deep learning for tackling classification problems, aiming to emulate the functionality of human neurons. However, many existing models that simulate neuron structures do not fully consider the non-linear relationships between dendrites and axons during signal transmission. To overcome this limitation, we introduce a novel deep learning model named dendritic SE-ResNet (DEN). This model simulates the construction of nonlinear signaling between dendrites and axons by combining biological attention mechanisms and the biologically interpretable neuron. In comparison to the original network, the proposed DEN exhibits a greater biological resemblance to the functioning of neurons. Experimental results further demonstrate that DEN outperforms some state-of-the-art deep neural network models in classification tasks. Compared to those models, our model attains a classification accuracy of 91.6%, marking an advancement of 2.7% over SE-ResNet. Additionally, our model demonstrates an F1-score of 92.4%, exhibiting an improvement of 4.4% compared to SE-ResNet.