Authors: Ziyuan Huang, Mark Newman, Maria Vaida, Srikar Bellur, Roozbeh Sadeghian, Andrew Siu, Hui Wang, Kevin Huggins
Abstract: 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.
Source: http://arxiv.org/abs/2501.04700v1
