  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.
