Gabor feature processing in spiking neural networks from retina-inspired data

In recent years, there has been a growing interest in dynamic vision sensors due to their incredible advantages in speed, computational cost and power consumption. These new vision sensors have been inspired from biological retinae and use asynchronous address-event representation for visual information instead of a series of snapshots taken from traditional frame-based devices. Spiking neurons are biologically-plausible artificial neurons that process information in sequences of time events and are particularly suited for processing address-event information. A novel and refined biologically-inspired Gabor feature approach based on spiking neural networks is presented here. This approach utilises the retina-inspired data from dynamic vision sensors with Gabor edge detection in a hierarchical structure that has been populated with Leaky-Integrate and Fire neurons that have been trained via the Remote Supervision Method. The number of active spiking neurons at each time instance depends on the number of time events. This idea provides a flexible approach that avoids unnecessary computations and complexity. The biologically-inspired model developed for this preliminary work has shown promising results and has laid the foundation for a rapid parallel object recognition model designed for the new retina-like address-event representation sensors.