This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system's most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.

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This paper presents an event-driven method for the simulation of the tempotron spiking neuron. We propose an efficient dummy-spike-based approach to deal with the twoexponential decay postsynaptic potential kernel. Experimental results show that event-driven simulation runs much faster than conventional time-driven simulation. We also propose a simple but efficient method to solve a same-timestamp problem encountered in event-driven simulation of a simplified tempotron neuron which uses only single-exponential decay.

The goal of this research is to explore the design and implementation of a hardware accelerated highlyefficient engine for the categorization of objects, using asynchronous event-based image sensor. The image sensor, namely Dynamic Vision Sensor (DVS), is equipped with temporal difference processing hardware. It outputs data in the format of binary event stream, in which 1 stands for a pixel on a motion object and 0 represents a still background pixel. The proposed system fully utilizes the precise timing information in the output of DVS. The asynchronous nature of this system frees computation and communication from the adamant clock timing in typical systems. Neuromorphic processing is used to extract cortex-like spike-based features through an event-driven MAX-like convolution network. Real-time light-weighted object recognition system is found more and more important, in particular in a number of emerging applications, such as in Unmanned Aerial Vehicle (UAV), where a recognition system that can detect and avoid obstacle; e-health applications such as human activity categorization; automobile application such as active car collision avoidance system, to name a few. A lot of research work has been focused on this before, mainly using traditional picture-based images sensor running at a high frame rate. Approaches like background subtraction can provide foreground object skeleton, followed by various techniques to represent the features in the image and at last, utilizing statics regression and classification algorithms, raw recognition results can be obtained. However, due to algorithm complexity and the large quantity of frame based image data, these algorithms have to be carried out on super powerful computers. This limits the application to wider usage, not to mention its high power consumption, mass and volume. In addition, conventional frame image sensor contains tons of data re-It is a great opportunity to express my in depth gratitude, to my supervisor, Prof Chen Shoushun, for his always insightful guidance, constant backing and unwavering support in my whole M.Eng program. I would like to extend my appreciation to Dr Yu Hang and all members in our research group, thank you all for your continuous support. Special thanks to Dr Zhao Bo for guiding me into neuromorphic engineering. My research could not complete without any one of you. Thank my parents and family for your endless and selfless love, it is you who encourage and support me throughout.