模仿扁虫神经结构，提升图像分类准确率
Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures
- 借鉴扁虫脑与两条神经索的生物结构设计新网络
- 在CIFAR-10和CIFAR-100上优于基线模型
- 适合对生物启发式架构感兴趣的研究者
本研究探索通过模拟扁虫（planarian）生物神经系统的进化模式，提升人工神经网络（ANN）在图像分类任务中的预测精度。选择广泛使用的深度与宽型残差网络（ResNet）作为基础模型。研究提出一种基于扁虫神经架构的新型神经网络，其结构包含一个脑及两条神经索，受此生物系统启发。该方法在CIFAR-10与CIFAR-100数据集上进行评估，结果表明所提模型在图像分类任务中表现优于基准模型。这一发现揭示了生物启发式神经架构在提升ANN性能方面的显著潜力，适用于多种应用场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。