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  • 标题:Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity
  • 本地全文:下载
  • 作者:Sou Nobukawa ; Haruhiko Nishimura ; Teruya Yamanishi
  • 期刊名称:Journal of Artificial Intelligence and Soft Computing Research
  • 电子版ISSN:2083-2567
  • 出版年度:2019
  • 卷号:9
  • 期号:4
  • 页码:283-291
  • DOI:10.2478/jaiscr-2019-0009
  • 出版社:Walter de Gruyter GmbH
  • 摘要:Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.
  • 关键词:spiking neural network ; spike timing-dependent plasticity ; dopamine-modulated spike timing-dependent plasticity ; pattern classification
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