A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition

A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust select...

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Bibliographic Details
Main Authors: Guangxuan Gao, Renyuan Liu, Mengying Wang, Qinbing Fu
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Biomimetics
Subjects:
collision detection
ON/OFF channels
feed-forward inhibition
computing efficiency
optimized LGMD
bio-robotics
Online Access:https://www.mdpi.com/2313-7673/9/11/650
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