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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance Targeting Neuromorphic Processors
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Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance Targeting Neuromorphic Processors

机译：针对UAV障碍避免靶向神经门处理器的尖峰神经网络中的参数优化和学习

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摘要

The Lobula giant movement detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses. Understanding the neural principles and network structure that leads to these fast and robust responses can facilitate the design of efficient obstacle avoidance strategies for robotic applications. Here, we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic dynamic vision sensor (DVS), which incorporates spiking frequency adaptation and synaptic plasticity mechanisms, and which can be mapped onto existing neuromorphic processor chips. However, as the model has a wide range of parameters and the mixed-signal analog-digital circuits used to implement the model are affected by variability and noise, it is necessary to optimize the parameters to produce robust and reliable responses. Here, we propose to use differential evolution (DE) and Bayesian optimization (BO) techniques to optimize the parameter space and investigate the use of self-adaptive DE (SADE) to ameliorate the difficulties of finding appropriate input parameters for the DE technique. We quantify the performance of the methods proposed with a comprehensive comparison of different optimizers applied to the model and demonstrate the validity of the approach proposed using recordings made from a DVS sensor mounted on an unmanned aerial vehicle (UAV).
机译：叶片巨型运动检测器（LGMD）是蝗虫的鉴定神经元，可检测迫在眉睫的物体并触发昆虫的逃生响应。了解导致这些快速和强大的反应的神经原理和网络结构可以促进用于机器人应用的有效障碍响应的设计。在这里，我们介绍由神经形态动态视觉传感器（DVS）的输出驱动的LGMD的神经形态尖峰神经网络模型，其包括尖峰频率适应和突触塑性机制，并且可以映射到现有的神经胸壁处理器芯片上。但是，由于该模型具有广泛的参数和用于实现模型的混合信号模数电路受变异性和噪声的影响，因此有必要优化参数以产生鲁棒和可靠的响应。在这里，我们建议使用差分演进（DE）和贝叶斯优化（BO）技术来优化参数空间，并调查自适应DE（SADE）的使用来改善寻找DE技术的适当输入参数的困难。我们量化了所提出的方法的性能，以全面比较应用于模型的不同优化器，并展示使用安装在无人机（UAV）上的DVS传感器所做的录音所提出的方法的有效性。

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