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A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception.

Jialan Hong¹, Xuelong Sun¹, Jigen Peng¹

¹Machine Life and Intelligence Research Centre, School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.

Biomimetics (Basel, Switzerland)

|March 27, 2024

Summary

This summary is machine-generated.

This study introduces a probabilistic lobula giant movement detector (LGMD) model to improve collision perception. The new model enhances noise tolerance in complex visual environments, outperforming conventional methods.

Keywords:

LGMD bio-inspired collision perception noise resistance probabilistic neural network

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Area of Science:

Computational Neuroscience
Robotics
Artificial Intelligence

Background:

Lobula giant movement detector (LGMD) models are used for collision perception.
Current LGMD models struggle with noisy signals in dynamic environments.
Biological synaptic transmission has inherent stochasticity that aids noise mitigation.

Purpose of the Study:

To develop a probabilistic LGMD (Prob-LGMD) model for enhanced collision perception.
To incorporate synaptic probability to capture signal uncertainty.
To improve noise tolerance in bio-inspired visual processing models.

Main Methods:

Developed a probabilistic LGMD (Prob-LGMD) model with probabilistic synaptic connections.
Tested the Prob-LGMD model against conventional LGMD models and engineering noise filters.
Utilized diverse visual stimuli including indoor and outdoor scenes with artificial noise.

Main Results:

The Prob-LGMD model demonstrated superior performance compared to all comparative methods.
Significant improvement in noise tolerance was observed with the proposed model.
The model effectively handles uncertainty in signal transmission and integration.

Conclusions:

The Prob-LGMD model offers a robust solution for collision perception in noisy conditions.
This probabilistic approach provides a straightforward yet effective enhancement for bio-inspired vision systems.
The findings highlight the potential of incorporating biological stochasticity into artificial systems.