A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

Abstract

Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

Accelerating Motion Perception Model Mimics the Visual Neuronal Ensemble of Crab

“…This section provide two groups of experiments to test the feasibility and limitations of the proposed model. Since the systematic experimental results of pre-synaptic neural network have been demonstrated in previous work [19], we only present the results of AMPM here.…”

“…We previously proposed a MLG1s neural network, which is used for perceiving local approaching motions [19]. The MLG1s neural network modelled the crab's visual pathways in transferring optic signals from retina to lamina, medulla and lobula complex.…”

“…Among aforementioned motion-sensitive neurons in arthropods, the sophisticated structure of monostratified lobula giant neurons type 1 (MLG1s) neuronal ensemble draws attention from both neuro-and computer scientists [12], [14], [15], [19]. Compared to the LGMD1 loomingsensitive neuron, which consists of one single neuron in lobula layer connected to post-synapses, the so-called MLG1s ensemble includes up to 16 MLG1 neurons covering the entire receptive field.…”

“…How to model such fascinating visual neuronal ensemble, however, leaves vacancy for bridging biological neural pathway to artificial vision system for solving real-world motion perception problems. Inspired by the crab's visual pathways and MLG1 neurons, the previous computational MLG1s neural network realizes the localization of approaching object [19]. Within this study, we further develop the previous MLG1s model and propose an accelerating motion perception model (AMPM) to mimic the visual neuronal ensemble of crab.…”

A monocular wide-field speed sensor inspired by the crabs’ visual system for traffic analysis

“…To date, the only bioinspired sensor based on visual processing in crustaceans is the collision detection system developed in [18], which is inspired by motion detection in MLG1 neurons of the crab Neohelice [19].…”