28 / 2017-01-10 20:25:03
A handwritten numeral recognition method based on STDP based with unsupervised learning
Spiking neural network;spike-timing-dependent-plasticity;numeral recognition;unsupervised learning;
终稿
永红 谢 / 广东工业大学
In order to sovle the problems of SNN lacking of biologically plausible mechnisms and performance,we present a SNN for numeral recognition based on mechanisms with increased biological plausibility,i.e.,Using unsupervised learning based on conductance rather than current-based synapses,lateral inhibition and adaptive spike thresholds.Experimental results show that the method in this paper has significant advantages in recognition accuracy in MNIST.It not only increases the accuracy of recognition,but also increases recognition efficiency over which uses BP neural network.
重要日期
  • 会议日期

    02月16日

    2017

    02月18日

    2017

  • 01月20日 2017

    初稿截稿日期

  • 01月30日 2017

    初稿录用通知日期

  • 02月10日 2017

    终稿截稿日期

  • 02月18日 2017

    注册截止日期

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