Giulioni Massimiliano, Lagorce Xavier, Galluppi Francesco, Benosman Ryad B
Department Technology and Health, Istituto Superiore di Sanità Rome, Italy.
Vision and Natural Computation Group, Institut National de la Santé et de la Recherche MédicaleParis, France; Sorbonne Universités, Institut de la Vision, Université de Paris 06 Pierre et Marie Curie, Centre National de la Recherche ScientifiqueParis, France.
Front Neurosci. 2016 Feb 16;10:35. doi: 10.3389/fnins.2016.00035. eCollection 2016.
Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.
估计移动物体的速度和方向是在动态世界中运行的智能体的关键组成部分。生物有机体通过源自其视网膜神经节细胞的神经连接来执行这项任务。在人工系统中，光流通常是通过比较视觉传感器捕获的两帧或多帧图像的活动来提取的。然而，设计出像生物系统中那样快速、稳健且高效的人工运动流检测器是一项具有挑战性的任务。受1965年巴洛和利维克提出的用于解释兔子视网膜中方向选择性神经节细胞的尖峰活动的架构启发，我们介绍了一种用于通过模拟神经形态多芯片系统进行稳健光流提取的架构。该任务由一个模拟积分发放神经元的前馈网络执行，其输入由对比敏感光感受器提供。计算由精确的尖峰发射时间支持，光流的提取基于附近视网膜神经元激活的时间延迟。模仿神经节细胞，我们的神经形态检测器在其输出尖峰模式中编码表观视觉运动的幅度和方向。在此，我们描述架构方面，讨论其延迟、可扩展性和稳健性属性，并证明一个由不匹配的精细模拟元件组成的网络可以可靠地从简单视觉场景中提取光流。这项工作展示了如何将精确的尖峰发射时间用作计算基础、生物启发和神经形态系统结合起来用于解决特定任务。
