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    • 7. 发明申请
    • NEURONAL DIVERSITY IN SPIKING NEURAL NETWORKS AND PATTERN CLASSIFICATION
    • 在神经网络和模式分类中的神经元多样性
    • WO2015088774A2
    • 2015-06-18
    • PCT/US2014067343
    • 2014-11-25
    • QUALCOMM INC
    • GUPTA VIKRAMLEVIN JEFFREY ALEXANDERLIAO EDWARD HANYU
    • G06N3/04
    • G06N3/08G06N3/049
    • A method for pattern recognition in a spiking neural network robust to initial network conditions includes creating a set of diverse neurons in a first layer to increase a diversity in a set of spike timings. An input corresponding to a pattern plus noise is presented at an input layer and represented as spikes. The spikes are received at the first layer and spikes are produced at the first layer based on the received spikes. The method also includes updating a weight of each synapse between an input layer neuron and an output layer neuron based on a spike timing difference between a spike at the input layer neuron and a spike at the output layer neuron. Further, the method includes classifying a spike pattern represented by a set of inter-spike intervals, regardless of noise in the spike pattern.
    • 在对初始网络条件稳健的加标神经网络中的模式识别的方法包括在第一层中创建一组不同的神经元以增加一组尖峰定时的分集。 对应于图案加噪声的输入在输入层处呈现并表示为尖峰。 尖峰在第一层被接收,并且基于接收的尖峰在第一层产生尖峰。 该方法还包括基于输入层神经元的尖峰与输出层神经元的尖峰之间的尖峰定时差来更新输入层神经元和输出层神经元之间的每个突触的权重。 此外,该方法包括分类由一组间穗间隔表示的尖峰图案,而不管尖峰图案中的噪声如何。