Luan Hao, Fu Qinbing, Zhang Yicheng, Hua Mu, Chen Shengyong, Yue Shigang
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China.
Machine Life and Intelligence Research Centre, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.
Front Neurosci. 2022 Jan 21;15:787256. doi: 10.3389/fnins.2021.787256. eCollection 2021.
Similar to most visual animals, the crab 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 , 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.
与大多数视觉动物类似，螃蟹主要依靠视觉信息来躲避捕食者、追踪猎物和选择配偶。因此，它需要专门的神经元来处理视觉信息并确定逼近物体的空间位置。在螃蟹中，已发现单分层小叶巨细胞1型（MLG1）神经元表现出对逼近物体的敏感性，并具有精细调节的编码空间位置信息的能力。MLG1神经元群体不仅能够感知逼近刺激的位置，还被认为能够持续影响运动方向，例如，相对于其位置逃离具有威胁性的逼近目标。与无法定位空间逼近的无脊椎动物中的正常逼近检测神经元相比，这些特定特征使MLG1神经元群体独一无二。对MLG1神经元群体进行建模不仅对于阐明此类神经回路功能的潜在机制至关重要，而且对于为机器人和车辆开发新的自主、高效、具有方向反应性的防撞系统也很重要。然而，针对实现类似于MLG1神经元群体特定功能的逼近空间定位，很少有计算建模工作。为了弥补这一差距，我们提出了一个MLG1神经元群体及其突触前视觉神经网络的模型，用于检测逼近物体的空间位置。该模型由16个均匀扇区组成，排列在一个圆形区域中，其灵感来自于16个MLG1感受野的自然排列，以编码和传递关于在视野不同位置具有动态扩展边缘的逼近物体的空间信息。所提出的模型对系统的真实世界视觉刺激的反应与MLG1神经元的许多生物学特征相匹配。系统实验表明，我们提出的MLG1模型能够有效且稳健地感知和定位逼近信息，这可能是在无碰撞的动态环境中进行交互的智能机器的一个有前途的候选模型。这项研究还揭示了一种新型的神经形态视觉传感器策略，该策略能够快速可靠地提取具有位置信息的逼近物体。
