This study examined the viability of enhancing the prediction accuracy of artificial neural networks (ANNs) in image classification tasks by developing ANNs with evolution patterns similar to those of biological neural networks. ResNet is a widely used family of neural networks with both deep and wide variants; therefore, it was selected as the base model for our investigation. The aim of this study is to improve the image classification performance of ANNs via a novel approach inspired by the biological nervous system architecture of planarians, which comprises a brain and two nerve cords. We believe that the unique neural architecture of planarians offers valuable insights into the performance enhancement of ANNs. The proposed planarian neural architecture-based neural network was evaluated on the CIFAR-10 and CIFAR-100 datasets. Our results indicate that the proposed method exhibits higher prediction accuracy than the baseline neural network models in image classification tasks. These findings demonstrate the significant potential of biologically inspired neural network architectures in improving the performance of ANNs in a wide range of applications.

本研究解决了人工神经网络在图像分类任务中的预测准确性问题，提出了一种基于平面动物生物神经网络架构的新型方法。研究结果表明，该方法在CIFAR-10和CIFAR-100数据集上的预测准确性优于基线神经网络模型，显示出生物启发的神经网络架构在提升ANN性能方面的巨大潜力。

平面动物神经网络：基础两侧生物演化模式塑造现代人工神经网络架构