Abstract: A computing platform (10), a method, and an apparatus (20) for spiking neural network (SNN) learning and simulation are provided. The computing platform (10) includes a neuron dynamics simulation module (11), a neuron conversion module (12), an SNN construction and weight learning module (13), and a neural network level parameter and weight access module (14). The neuron dynamics simulation module (11) simulates changing features of neurons. The neuron conversion module (12) performs operations on a calculation graph. The SNN construction and weight learning module (13) updates and iterates connection weights. The neural network level parameter and weight access module (14) stores overall network detail parameters.

Abstract: A target tracking method and a target tracking system of a spiking neural network based on an event camera are provided. The method includes: acquiring a data stream of asynchronous events in a high dynamic scene of a target by an event camera as input data; dividing the data stream of the asynchronous events into synchronous event frames with millisecond time resolution; training a twin network based on a spiking neural network by a gradient substitution algorithm with a target image as a template image and a complete image as a searched image; and tracking the target by a trained twin network with interpolating a result of feature mapping to up-sample and obtaining the position of the target in an original image. The twin network includes a feature extractor and a cross-correlation calculator.

Abstract: In an object recognition method, an object recognition device obtains AER data of a to-be-recognized object, wherein the AER data includes a plurality of AER events of the to-be-recognized object, each AER event comprising a timestamp and address information. The object recognition device extracts a plurality of feature maps of the AER data. Each feature map including partial spatial information and partial temporal information of the to-be-recognized object, and the partial spatial information and the partial temporal information are obtained based on the timestamp and the address information of each AER event. The object recognition device then recognizes the to-be-recognized object based on the plurality of feature maps of the AER data.