Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures

TL;DR

This paper introduces a biologically inspired neural network architecture based on planarian neural patterns, which improves image classification accuracy on CIFAR datasets compared to traditional models.

Contribution

It proposes a novel neural network architecture inspired by planarian nervous systems, enhancing prediction accuracy in image classification tasks.

Findings

Higher accuracy on CIFAR-10 and CIFAR-100 datasets

Biologically inspired architecture outperforms baseline models

Demonstrates potential of biological neural patterns in ANN design

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.