Bio-inspired computational model for direction and speed detection

Abstract

This article introduces a biologically-inspired model capable of detecting both an object’s motion direction and speed, based on retinal neural mechanisms verified through biological experiments. It aims to address the interpretability issues present in current deep learning models. The proposed Motion Detection Neuron (MDN) model, inspired by early research on the retina’s direction and speed sensitivity, replicates the motion detection functions of the retina and primary visual cortex. The design of the MDN, inspired by the layered structure of the retina and incorporating various cell types and functions, has been validated through biological experimentation, providing it with robust biological interpretability. Extensive experiments have been conducted to assess the MDN’s detection accuracy and robustness against various types of noise. Additionally, to verify that the MDN not only offers enhanced biological interpretability but also maintains detection accuracy comparable to leading deep learning algorithms, we compared its performance with that of LeNet ,EfficientNet and RegNet under identical conditions. The results show that the MDN not only provides better biological interpretability and lower hardware demands but also excels in accuracy under specific conditions, comparable to advanced deep learning algorithms.