Investigating Refractoriness in Collision Perception Neuronal Model

Hua, Mu, Fu, Qinbing, Duan, Wenting and Yue, Shigang (2021) Investigating Refractoriness in Collision Perception Neuronal Model. In: 2021 International Joint Conference on Neural Networks (IJCNN), 18-22 July 2021, Shenzhen, China.

Full content URL:

Investigating Refractoriness in Collision Perception Neuronal Model
Authors' Accepted manuscript
Investigating-Refractoriness-in-Collision-Perception-Neuronal-Model.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Currently, collision detection methods based on visual cues are still challenged by several factors including ultrafast approaching velocity and noisy signal. Taking inspiration from nature, though the computational models of lobula giant
movement detectors (LGMDs) in locust’s visual pathways have demonstrated positive impacts on addressing these problems, there remains potential for improvement. In this paper, we propose a novel method mimicking neuronal refractoriness, i.e. the refractory period (RP), and further investigate its functionality and efficacy in the classic LGMD neural network model for collision perception. Compared with previous works, the two phases constructing RP, namely the absolute refractory period (ARP) and relative refractory period (RRP) are computationally implemented through a ‘link (L) layer’ located between the photoreceptor and the excitation layers to realise the dynamic characteristic of RP in discrete time domain. The L layer, consisting of local time-varying thresholds, represents a sort of mechanism that allows photoreceptors to be activated individually and selectively by comparing the intensity of each photoreceptor to its corresponding local threshold established by its last output. More specifically, while the local threshold can merely be augmented by larger output, it shrinks exponentially over time. Our experimental outcomes show that, to some extent, the investigated mechanism not only enhances the LGMD model in terms of reliability and stability when faced with ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt-Pepper noise. This research demonstrates the modelling of refractoriness is effective in collision perception neuronal models, and promising to address the aforementioned collision detection challenges.

Keywords:Collison Perception, Neuronal models
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:46692
Deposited On:11 Oct 2021 13:19

Repository Staff Only: item control page