Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity

• Autorzy: Nobukawa Sou , Nishimura Haruhiko , Yamanishi Teruya

Identyfikatory

DOI

10.2478/jaiscr-2019-0009

• Języki publikacji: EN

Abstrakty

EN

Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopaminemodulated 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.

Słowa kluczowe

EN

spiking neural network; spike timing-dependent plasticity; dopamine-modulated spike timing-dependent plasticity; pattern classification

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2019
• Tom: Vol. 9, No. 4
• Strony: 283--291
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Nobukawa Sou

• Department of Computer Science, Chiba Institute of Technology 2-17-1 Tsudanuma, Narashino, Chiba, 275–0016 Japan

autor

Nishimura Haruhiko

• Graduate School of Applied Informatics, University of Hyogo 7–1–28 Chuo-ku, Kobe, Hyogo, 650–8588 Japan

autor

Yamanishi Teruya

• Department of Management and Information Sciences, Fukui University of Technology 3–6–1 Gakuen, Fukui, Fukui, 910–8505 Japan

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).