Efficient Motion Symbol Detection and Multikernel Learning for AER Object Recognition

Address event representation (AER) vision sensors process visual information by monitoring changes in light intensity and generating event streams. In this article, a threshold mechanism based on the statistical results of the peak membrane potentials is proposed to improve the accuracy and efficiency of the motion symbol detection (MSD) method based on the leaky integrate-and-fire (LIF) neuron model and a peak spiking monitoring unit (PSMU). A multikernel learning algorithm based on the tempotron rule, namely, MK-tempotron, is proposed to improve the antinoise performance of the classifier. In MK-tempotron, different kernels are applied to calculate the post-synaptic membrane potentials (PSPs) according to different input spiking patterns, to counteract the impact of noise on the spiking activities, so that the synaptic weights can be changed in the direction of correct firing. We verified the effectiveness of the threshold mechanism and multikernel learning on a variety of data sets. The experimental results show that among the several recent algorithms based on the temporal encoding and spiking neural network classifier, which have advantages in power consumption and network latency, our method achieved the best accuracy on MNIST-DVS (100 ms) and N-CARS, and it also achieved competitive results on N-MNIST, CIFAR10-DVS, Posture-DVS, Poker-DVS, and MNIST-DVS (200 ms/500 ms/2000 ms).