涡虫神经网络：来自基础两侧对称动物、塑造现代人工神经网络架构的演化模式
Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures
AI总结：
本文受涡虫由一个脑和两条神经索构成的神经系统启发，以ResNet为基础提出涡虫式神经网络，并在CIFAR-10/100上验证其图像分类准确率优于基线模型。
摘要：
本研究考察了通过开发具有与生物神经网络相似演化模式的人工神经网络（ANN），来提升其在图像分类任务中预测准确率的可行性。ResNet是一个被广泛使用的神经网络家族，兼具深度和宽度变体，因此被选为本研究的基础模型。本研究旨在通过一种受涡虫生物神经系统架构启发的新方法来提升ANN的图像分类性能；涡虫的神经系统由一个脑和两条神经索组成。我们认为，涡虫独特的神经架构为提升ANN性能提供了有价值的洞见。所提出的基于涡虫神经架构的神经网络在CIFAR-10和CIFAR-100数据集上进行了评估。结果表明，在图像分类任务中，所提方法相比基线神经网络模型表现出更高的预测准确率。这些发现证明，受生物启发的神经网络架构在广泛应用中提升ANN性能方面具有显著潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
