## 5 Discussion  
The platform developed in this paper has successfully demonstrated the closed-loop communication between DVS, SpiNNaker and a servo motor by using a single microcontroller. Compared to unidirectional communication of the FPGA interface, the developed solution supports bidirectional data flow and can control external actuators according to the received spikes from SNNs. The FPGA interface is based on a high-cost RoggedStone2 development kit, while our solution is based on the Arduino platform, which is more economical. Figure 10: The spikes of the output layer of the SNN in one trial. Apart from this solution, another microcontroller interface developed by the UM and TUM also supports bidirectional communication and control actuators. However, it requires the assistance of a CPLD, which increases the data transmission speed between SpiNNaker and the microcontroller but also increases the difficulty of further development. Their solution is based on an ARM Cortex-M4 microcontroller with a 168 MHz clock speed, whilst we use a Cortex-M3 microcontroller with an 86 MHz clock speed. Thus, the data transition of our solution is slower than the speed of the interface designed by UM and TUM. On the other side, our solution does not need the second chip, which means lower power consumption and higher stability. Additionally, the microcontroller developed by UM and TUM has 5 pre-defined peripheral ports for 2 DVSs and 3 motors. This design simplifies the connections with DVSs and motors but results in a limited capacity for other actuators and sensors. The microcontroller board used in our solution has 54 GPIOs and built-in libraries for external devices. Compared to the robot goalie based on a PC (and DVS), the neuromorphic system that uses SpiNNaker as the processor has a lower power consumption, and it is completely controlled by an SNN running on a SpiNNaker, not using any PC processing power. Therefore, our system is portable and readily implementable on autonomous, battery-operated robotic applications. Admittedly for a small neural network as ours it would be possible to implement it on a microcontroller and thus the power consumption could be even lower than by using SpiNNaker. However, SpiNNaker is much easier to be used in developing a new SNN for our platform because of already existing PyNN software interface, and the power consumption is still acceptable for robotic applications. Our solution can also be compared with other neuromorphic systems for tracking objects by using artificial neural networks. For example, [PERSON] et al. [24] have developed a recurrent neural network (RNN) for predicting the trajectory of a bouncing ball (the hardware used was ATIS event camera and iCub robot [16; 21; 24]). The input events of this neural network were also pre-processed by sub-sampling. This strategy reduces the computational complexity, but can affect the resolution precision. The RNN was using the long-short term memory (LSTM) learning rule [24], which indicates the current output is depended on previous input events. It is similar to our algorithm where the current prediction of the position of ball's arrival is based on 20 latest spikes received from SpiNNaker (Figure 4). Prediction of the upcoming trajectory of a tracked object was done in an asynchronous fashion. The RNN gives both horizontal and vertical prediction, but our algorithm only focuses on horizontal information. The training set and the testing set of the RNN are recorded data, and its performance of real-time events has not been investigated. The camera (DVS) used to capture the information of moving objects is of the neuromorphic type, which exploits data-driven, event-based updates--the same as SNNs. It generates inherently sparse data, which is important for low latency and energy-efficient applications. Compared to frame-based cameras, bandwidth and computational complexity are greatly reduced. Furthermore, DVS is much better for the task of object tracking since the static background does not generate data.