Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to loss of direction-selectivity of individual TDEs in textured environments. Here we propose an augmented 3-point TDE (TDE-3) with additional inhibitory input that makes TDE-3 direction-selectivity robust in textured environments. We developed a procedure to train the new TDE-3 using backpropagation through time and surrogate gradients to linearly map input velocities into an output spike count or an Inter-Spike Interval (ISI). Our work is the first instance of training a spiking neuron to have a specific ISI. Using synthetic data we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise. ISI turns out to be more robust towards variation in spatial frequency, whereas the spike count is a more reliable training signal in the presence of noise. We performed the first in-depth quantitative investigation of optical flow coding with TDE and compared TDE-2 vs TDE-3 in terms of energy-efficiency and coding precision. Results show that on the network level both detectors show similar precision (20 degree angular error, 88% correlation with ground truth). Yet, due to the more robust direction-selectivity of individual TDEs, TDE-3 based network spike less and hence is more energy-efficient. Reported precision is on par with model-based methods but the spike-based processing of the TDEs provides allows more energy-efficient inference with neuromorphic hardware.

翻译：运动检测是机器人系统感知并导航其环境所需的首要任务。文献中提出的生物启发式神经形态时间差编码器（TDE-2）将基于事件的传感器与处理器结合脉冲神经网络，通过提取空间两点间的时间相关性，实现实时且节能的运动检测。然而，在算法层面，这种设计导致在纹理环境中单个TDE的方向选择性丢失。为此，我们提出一种增强型三点式TDE（TDE-3），通过增加抑制性输入，使其方向选择性在纹理环境中具有鲁棒性。我们开发了一套训练新TDE-3的流程，利用时间反向传播和替代梯度，将输入速度线性映射为输出脉冲计数或脉冲间间隔（ISI）。本工作是首次训练脉冲神经元以产生特定ISI的实例。利用合成数据，我们比较了基于脉冲计数与ISI的训练和推理方法在刺激动态范围、空间频率和噪声水平变化下的表现。结果表明，ISI对空间频率变化更具鲁棒性，而脉冲计数在存在噪声时是更可靠的训练信号。我们首次对基于TDE的光流编码进行了深入的定量研究，并从能效和编码精度两方面比较了TDE-2与TDE-3。结果显示，在网络层面，两种检测器精度相近（20度角度误差，与真实值的相关性达88%）。然而，由于单个TDE方向选择性更为鲁棒，基于TDE-3的网络发放更少脉冲，因而能效更高。所报告的精度与基于模型的方法相当，但TDE的脉冲处理特性使其在神经形态硬件上能够实现更节能的推理。