A look into feedback neural computation upon collision selectivity

A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming

In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust's visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existing bio-inspired collision detection neural networks encounter challenges posed by jitter streaming, a phenomenon commonly experienced, for example, when a ground robot moves across uneven terrain. Visual inputs from jitter streaming induce significant fluctuations in grey values, distracting existing bio-inspired networks from extracting visually looming features. To overcome this limitation, we derive inspiration from the potential of feedback loops to enable the brain to generate a coherent visual perception. We introduce a novel dynamic temporal variance feedback loop regulated by scalable functional into the traditional bio-inspired collision detection neural network. This feedback mechanism extracts dynamic temporal variance information from the output of higher-order neurons in the conventional network to assess the fluctuation level of local neural responses and regulate it by a scalable functional to differentiate variance induced by incoherent visual input. Then the regulated signal is reintegrated into the input through negative feedback loop to reduce the incoherence of the signal within the network. Numerical experiments substantiate the effectiveness of the proposed feedback loop in promoting collision detection against jitter streaming. This study extends the capabilities of bio-inspired collision detection neural networks to address jitter streaming challenges, offering a novel insight into the potential of feedback mechanisms in enhancing visual neural abilities.

Rigid propagation of visual motion in the insect's neural system

In the pursuit of developing an efficient artificial visual system for visual motion detection, researchers find inspiration from the visual motion-sensitive neural pathways in the insect's neural system. Although multiple proposed neural computational models exhibit significant performance aligned with those observed from insects, the mathematical basis for how these models characterize the sensitivity of visual neurons to corresponding motion patterns remains to be elucidated. To fill this research gap, this study originally proposed that the rigid propagation of visual motion is an essential mathematical property of the models for the insect's visual neural system, meaning that the dynamics of the model output remain consistent with the visual motion dynamics reflected in the input. To verify this property, this study uses the small target motion detector (STMD) neural pathway - one of the visual motion-sensitive pathways in the insect's neural system - as an exemplar, rigorously demonstrating that the dynamics of translational visual motion are rigidly propagated through the encoding of retinal measurements in STMD computational models. Numerical experiment results further substantiate the proposed property of STMD models. This study offers a novel theoretical framework for exploring the nature of the visual motion perception underlying the insect's visual neural system and brings an innovative perspective to the broader research field of insect visual motion perception and artificial visual systems.

A computationally efficient and robust looming perception model based on dynamic neural field

There are primarily two classes of bio-inspired looming perception visual systems. The first class employs hierarchical neural networks inspired by well-acknowledged anatomical pathways responsible for looming perception, and the second maps nonlinear relationships between physical stimulus attributes and neuronal activity. However, even with multi-layered structures, the former class is sometimes fragile in looming selectivity, i.e., the ability to well discriminate between approaching and other categories of movements. While the latter class leaves qualms regarding how to encode visual movements to indicate physical attributes like angular velocity/size. Beyond those, we propose a novel looming perception model based on dynamic neural field (DNF). The DNF is a brain-inspired framework that incorporates both lateral excitation and inhibition within the field through instant feedback, it could be an easily-built model to fulfill the looming sensitivity observed in biological visual systems. To achieve our target of looming perception with computational efficiency, we introduce a single-field DNF with adaptive lateral interactions and dynamic activation threshold. The former mechanism creates antagonism to translating motion, and the latter suppresses excitation during receding. Accordingly, the proposed model exhibits the strongest response to moving objects signaling approaching over other types of external stimuli. The effectiveness of the proposed model is supported by relevant mathematical analysis and ablation study. The computational efficiency and robustness of the model are verified through systematic experiments including on-line collision-detection tasks in micro-mobile robots, at success rate of 93% compared with state-of-the-art methods. The results demonstrate its superiority over the model-based methods concerning looming perception.

SLoN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels

Looming perception, the ability to sense approaching objects, is crucial for the survival of humans and animals. After hundreds of millions of years of evolutionary development, biological entities have evolved efficient and robust looming perception visual systems. However, current artificial vision systems fall short of such capabilities. In this study, we propose a novel spiking neural network for looming perception that mimics biological vision to communicate motion information through action potentials or spikes, providing a more realistic approach than previous artificial neural networks based on sum-then-activate operations. The proposed spiking looming perception network (SLoN) comprises three core components. Neural encoding, known as phase coding, transforms video signals into spike trains, introducing the concept of phase delay to depict the spatial-temporal competition between phasic excitatory and inhibitory signals shaping looming selectivity. To align with biological substrates where visual signals are bifurcated into parallel ON/OFF channels encoding brightness increments and decrements separately to achieve specific selectivity to ON/OFF-contrast stimuli, we implement eccentric down-sampling at the entrance of ON/OFF channels, mimicking the foveal region of the mammalian receptive field with higher acuity to motion, computationally modeled with a leaky integrate-and-fire (LIF) neuronal network. The SLoN model is deliberately tested under various visual collision scenarios, ranging from synthetic to real-world stimuli. A notable achievement is that the SLoN selectively spikes for looming features concealed in visual streams against other categories of movements, including translating, receding, grating, and near misses, demonstrating robust selectivity in line with biological principles. Additionally, the efficacy of the ON/OFF channels, the phase coding with delay, and the eccentric visual processing are further investigated to demonstrate their effectiveness in looming perception. The cornerstone of this study rests upon showcasing a new paradigm for looming perception that is more biologically plausible in light of biological motion perception.

Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications.