Zhejiang University

Zhejiang Lab

Huawei Technologies Co., Limited

Wuhan Fortune Science and Technology Co., Ltd.

Gang Pan

Yu Qi

Yueming Wang

Huajin Tang

Dong Xing

Shuiguang Deng

Zhaohui Wu

Ying Li

Jie Cheng

Jumin Tu

Lei Jiang

Tao Fang

Xiao Wang

Fengjuan Wang

Pan Lv

Min Kang

Qiaosheng Zhang

Mengwen Yuan

Yujing Lu

Wenyi Zhao

Cunle Qian

Yaoyao Hao

Chaofei Hong

Qianhui Liu

Jiwen Zhang

Haibo Ruan

De Ma

Hang Yu

Mengxiao Zhang

Xianyin Zhang

Kedi Xu

Yiwen Wang

Xiaoxiang Zheng

Xiaozhi Wu

Xinyun Zhu

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.

Target tracking method and system of spiking neural network based on event camera

A method and a device of intra-chip routing of neural tasks for an operating system of a brain-inspired computer are provided. The method includes determining an area defined by target cores, and determining target cores in a row furthest from an edge routing area; determining whether the target cores need to be configured with relay routing; searching nearest edge routing cores in the edge routing area for all the target cores in the area defined by the target cores; configuring the target cores in a far-to-near principle; and searching relay routing cores and the nearest edge routing cores by a shortest path manner and a maximum step length of a single routing manner.

Device and method of intra-chip routing of neural tasks for operating system of brain-inspired computer

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.

Object recognition method and apparatus

The present invention discloses an adaptive brain-computer interface decoding method based on multi-model dynamic ensemble, where a traditional state-space model is improved, and a set of measurement functions instead of one fixed measurement function are used to dynamically characterize a relationship between observation variables and state variables; and, by using a pool of linear and nonlinear decoders, and in a decoding process of a brain-computer interface system, decoders are automatically switched according to the data, so as to realize adaptive brain signal decoding. Through the above multi-model ensemble strategy, linear and nonlinear decoder capabilities can be integrated, the accuracy and stability of the brain-computer interface system can be improved, and decoding unstability caused by the non-stationary neural signal of the brain-computer interface system can be solved to a certain extent.

Adaptive brain-computer interface decoding method based on multi-model dynamic integration

The present invention discloses an online neuron spike sorting method based on neuromorphic computing, which converts neuron spike signals collected from the cerebral cortex into spike signals through field coding, classifies different waveforms and corresponding time stamps by means of spiking neural networks, and realizes online neuron spike sorting; at the same time, the online update method of spiking neural network is used to adapt to the online changes of neuronal spike waveform and improve the accuracy of long-term online neuronal spike sorting. This method has fast computational speed, which can improve the speed of spike sorting process, maintain high consistency in classification on different datasets, and facilitate the deployment of implanted chips.

Online neuron classification method based on neuromorphic computing

The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is W, a loop connection weight inside the middle layer is W, a readout weight from the middle layer to the output layer is W; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Wfrom the input layer to the middle layer; (2-2) Setting the loop connection weight Winside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wfrom the middle layer to the output layer, and establishing a mapping between the middle layer liquid information R(t) and the output motion information, and finally outputting a predicted motion trajectory. The present invention can quickly train a model in a relatively short time, predict the arm motion trajectory in real time, and achieve an improvement in efficiency and accuracy.

Brain machine interface decoding method based on spiking neural network

The present invention is related to a method for prediction of cortical spiking neural signal, comprising the following steps: 1) pretreatment of spiking neural signal, 2) modeling of posterior cortical spiking neural signal generation probability, 3) model accuracy measurement, 4) model optimization with the help of numerical gradient descent and 5) prediction of posterior cortical spiking neural signal with iterative calculation. The prediction method aims to incorporate natural features of point process of spiking neural signal into the target for optimization of prediction model to improve model's capability in predicting neural spike trains.

Method for prediction of cortical spiking trains

The present invention provides a kind of rice endogenous bentazon and sulfonylurea herbicide resistant gene (Cyp81A6 gene), and its functional conservative variants, the biological activity subfragments or derivatives with the same function. It also provides a kind of method to prevent the selling mixtures during hybrid seed production. It also provides the novel means of the directional genetic manipulation and the improvement of biological traits.

Rice bentazon and sulfonylurea herbicide resistant gene Cyp81a6

Growing community of inventors

Hangzhou, China

Gang Pan