An Artificial Neural Network for Image Classification Inspired by the Aversive Olfactory Learning Neural Circuit in Caenorhabditis elegans.

ABSTRACT: This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning neural circuit in Caenorhabditis elegans (C. elegans). Although artificial neural networks (ANNs) have demonstrated remarkable performance in various tasks, they still encounter challenges including excessive parameterization, high training costs and limited generalization capabilities, etc. C. elegans, boasting a simple nervous system consisting of merely 302 neurons, is capable of exhibiting complex behaviors such as aversive olfactory learning. This research pinpoints key neural circuit related to aversive olfactory learning in C. elegans by means of behavioral experiment and high-throughput RNA sequencing, and then translates it into an architecture of ANN

SUBMITTER: Wang X 

PROVIDER: S-EPMC11831476 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature