Spiking neural networks (SNNs) more closely mimic the human brain than artificial neural networks (ANNs). For SNNs, time-to-first-spike (TTFS) encoding, which represents the output values of neurons based on the timing of a single spike, has been proposed as a promising model to reduce power consumption. Adversarial attacks that can lead ANNs to misrecognize images have been reported in many studies. However, the characteristics of TTFS-based SNNs trained using a backpropagation algorithm against adversarial attacks have not yet been clarified. In particular, the dependence of the robustness against adversarial attacks on spike timings has not been investigated. In this study, we investigated the robustness of SNNs against adversarial attacks and compared it with that of an ANN. We found that SNNs trained with the appropriate temporal penalty settings are more robust against adversarial images than ANNs.

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In this paper, we propose an FPGA-based collision warning system for advanced automobile driver assistance systems or autonomous moving robots. The system consists of three function blocks: coarse edge detection using a resistive-fuse network, moving-object detection inspired by neuronal propagation in the hippocampus, and danger evaluation and collision warning using fuzzy inference. The first two functions are implemented in FPGAs. The system can detect moving objects with a speed range of 3-192 km/h with a sampling period of 30 ms for an input image of 320 × 256 pixels, and can output a warning against dangerous regions in the input image.