In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory by adding a simple read-out layer composed of polynomial regressions, and trained in a supervised manner. Hence, we show that a SNN receiving inputs from an event-based sensor can extract relevant spatio-temporal patterns to process and predict ball trajectories.

中文翻译：

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基于尖峰神经网络的基于事件的轨迹预测

近年来，基于事件的传感器已与尖峰神经网络（SNN）相结合，以创建新一代的生物启发式人工视觉系统。这些系统可以实时处理时空数据，并且具有很高的能源效率。在这项研究中，我们使用了一种新的基于混合事件的相机，并结合了一个由依赖于峰值定时的可塑性学习规则训练的多层峰值神经网络。我们表明，神经元以无人监督的方式从重复和相关的时空模式中学习，并对运动特征（例如方向和速度）具有选择性。然后，通过添加由多项式回归组成的简单读出层并以监督方式进行训练，可以将这种运动选择性用于预测球的轨迹。因此，