Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

Sou Nobukawa 1 , Haruhiko Nishimura 2 , and Teruya Yamanishi 3
  • 1 Department of Computer Science, Chiba Institute of Technology, , 275–0016, Chiba, Japan
  • 2 Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
  • 3 Department of Management and Information Sciences, Fukui University of Technology, Japan


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.

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