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Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.

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Ordered mesoporous carbon (OMC) supported well-dispersed PtFe x nanoparticles with a controllable size distribution were prepared via a modified polyol synthesis route, using hexachloroplatinic acid and ferric chloride as Pt and Fe source, and ethylene glycol as a reducing agent. The catalytic activities relevant to direct methanol fuel cell of the PtFe x /OMC composites were investigated using cyclic voltammetry, single-cell proton exchange membrane fuel cell (PEMFC) test and electrochemical impedance spectroscopy (EIS) technique. Due to the existence of more Pt 0 species and Fe ion corrosion caused by the formation of the alloyed PtFe x catalyst, Pt 0 can provide the more active sites for methanol oxidation reaction, and the methanol oxidation activity of the PtFe x /OMC electrode is evidenced to be enhanced by the increased anodic peak current with increasing the incorporation content of Fe. The oxygen reduction reaction (ORR) current density of 0.662 A cm À2 and power density of 237.2 mW cm À2 generated by the PtFe 3 /OMC sample are more than two times the values of 0.32 mA cm À2 and 102.6 mW cm À2 by the Pt/OMC sample. The PtFe 3 /OMC catalyst in 0.5 M H 2 SO 4 + 1 M CH 3 OH displays the highest specific catalytic activity of 100.6 mA m À2 , which is almost 3 times lower than that of 283.7 mA m À2 in 0.5 M H 2 SO 4 . The enhanced higher activity for the PtFe 3 /OMC sample can be firstly attributed to a highly homogeneous dispersion of the PtFe 3 nanoparticles on the mesoporous channels within OMC, such PtFe 3 nanoparticles with a diameter of 3.3 nm can accelerate the formation of Pt-OH groups. Meanwhile, the alloyed PtFe 3 nanoparticles can provide a lower onset potential for the electrooxidation of CO/H 2 than that of pure Pt, and would contribute more to the promotion of C-H breaking and CO ad tolerance. Furthermore, the larger surface area, the favorable pore structure and the structural integrity between the PtFe 3 nanoparticles and the OMC matrix, will effectively facilitate the transportation of reactants and products in liquid electrochemical reactions.

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