- Tytuł:
- Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity
- Autorzy:
-
Nobukawa, Sou
Nishimura, Haruhiko
Yamanishi, Teruya - Powiązania:
- https://bibliotekanauki.pl/articles/91886.pdf
- Data publikacji:
- 2019
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
spiking neural network
spike timing-dependent plasticity
dopamine-modulated spike timing-dependent plasticity
pattern classification - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 4; 283-291
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł