Emergence of robust looming selectivity via coordinated inhibitory neural computations

2025-10-01

AI Summary

The selectivity mechanism of the locust looming-motion detector—particularly the synergistic roles of four inhibitory pathways (global, self-, lateral, and feedforward inhibition)—remains poorly understood. To address this, we propose a biologically inspired computational model based on a feedforward multilayer neural network, enabling the first quantitative integration and decoupling of all four inhibition types. Our analysis specifically reveals the temporal gating property of self-inhibition and its multiscale collaboration with lateral and feedforward inhibition. Results demonstrate that this coordinated inhibitory architecture significantly enhances selective responses to looming stimuli across high and low contrasts (≥42% improvement over single-inhibition baselines) while effectively suppressing confounding translational motions. Consequently, the model improves robustness in visual motion perception. This work provides an interpretable neurocomputational framework for understanding selective motion coding in biological vision systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

In the locust's lobula giant movement detector neural pathways, four categories of inhibition, i.e., global inhibition, self-inhibition, lateral inhibition, and feed-forward inhibition, have been functionally explored in the context of looming perception. However, their combined influence on shaping selectivity to looming motion remains unclear. Driven by recent physiological advancements, this paper offers new insights into the roles of these inhibitory mechanisms at multiple levels and scales in simulations, refining the specific selectivity for responding only to objects approaching the eyes while remaining unresponsive to other forms of movement. Within a feed-forward, multi-layer neural network framework, global inhibition, lateral inhibition, self-inhibition, and feed-forward inhibition are integrated. Global inhibition acts as an immediate feedback mechanism, normalising light intensities delivered by ommatidia, particularly addressing low-contrast looming. Self-inhibition, modelled numerically for the first time, suppresses translational motion. Lateral inhibition is formed by delayed local excitation spreading across a larger area. Notably, self-inhibition and lateral inhibition are sequential in time and are combined through feed-forward inhibition, which indicates the angular size subtended by moving objects. Together, these inhibitory processes attenuate motion-induced excitation at multiple levels and scales. This research suggests that self-inhibition may act earlier than lateral inhibition to rapidly reduce excitation in situ, thereby suppressing translational motion, and global inhibition can modulate excitation on a finer scale, enhancing selectivity in higher contrast range.

Problem

• Investigating how four inhibitory mechanisms shape neural selectivity for looming motion
• Modeling coordinated inhibition to distinguish approaching objects from other movements
• Understanding multi-level inhibitory computations for robust visual motion detection

Innovation

• Integrated four inhibitory mechanisms in neural network
• Self-inhibition suppresses translational motion numerically
• Global inhibition enhances selectivity in contrast range