Zhenshan Bing

TL;DR: This paper surveys the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications, and highlights the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities.

TL;DR: This paper introduces an end to end learning approach of spiking neural networks for a lane keeping vehicle that considers the reward-modulated spike-timing-dependent-plasticity (R-STDP) as a promising solution in training SNNs, since it combines the advantages of both reinforcement learning and the well-known STDP.

TL;DR: This work used a Dynamic and Active Pixel Sensor (DAVIS), whose two channels concurrently output conventional gray-scale frames and asynchronous low-latency temporal contrast events of light intensity, to detect pedestrians in a traffic monitoring scenario and reached higher average precision by using the fusion approach.

TL;DR: Extensive simulations and prototype experiments finally demonstrated that smooth slithering gait transition can be effectively achieved using the proposed CPG-based control method without generating undesired locomotion and abnormal torque.

TL;DR: The first neuromorphic vision based multivehicle detection and tracking system in ITS is proposed and the performance of the system is evaluated with a dataset recorded by a neuromorph vision sensor mounted on a highway bridge.