| Presentation | 2022-06-09 Reservoir computing with spiking neural networks and reward-modulated STDP Takayuki Tsurumi, Gouhei Tanaka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In a previous study, it was verified that tasks requiring nonlinearity and working memory can be performed using reward-modulated Hebbian learning (RMHL) as a learning rule for echo state networks (ESN), which is a representative model for reservoir computing. Also, some studies have used liquid state machines (LSM) for reinforcement learning, which is a more biologically relevant model for reservoir computing based on spiking neural networks (SNN) than the ESNs. However, reward-modulated STDP (RM-STDP), which is a learning rule for SNN similar to RMHL, has not been used as a learning rule in those studies. In this presentation, we will show an experiment in which a reservoir computing model based on an SNN reservoir and RM-STDP, is applied to the linearly inseparable XOR problem. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | spiking neural network / reward-modulated STDP / reservoir computing / reinforcement learning |
| Paper # | NLP2022-7,CCS2022-7 |
| Date of Issue | 2022-06-02 (NLP, CCS) |
| Conference Information | |
| Committee | CCS / NLP |
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| Conference Date | 2022/6/9(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Megumi Akai(Hokkaido Univ.) / Akio Tsuneda(Kumamoto Univ.) |
| Vice Chair | Masaki Aida(TMU) / Hidehiro Nakano(Tokyo City Univ.) / Hiroyuki Torikai(Hosei Univ.) |
| Secretary | Masaki Aida(TDK) / Hidehiro Nakano(Shibaura Insti. of Tech.) / Hiroyuki Torikai(Sojo Univ.) |
| Assistant | Tomoyuki Sasaki(Shonan Instit. of Tech.) / Hiroyasu Ando(Tsukuba Univ.) / Miki Kobayashi(Rissho Univ.) / " Hiroyuki YASUDA(The Univ. of Tokyo) / Yuichi Yokoi(Nagasaki Univ.) / Yoshikazu Yamanaka(Utsunomiya Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Complex Communication Sciences / Technical Committee on Nonlinear Problems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Reservoir computing with spiking neural networks and reward-modulated STDP |
| Sub Title (in English) | |
| Keyword(1) | spiking neural network |
| Keyword(2) | reward-modulated STDP |
| Keyword(3) | reservoir computing |
| Keyword(4) | reinforcement learning |
| 1st Author's Name | Takayuki Tsurumi |
| 1st Author's Affiliation | The University of Tokyo(UTokyo) |
| 2nd Author's Name | Gouhei Tanaka |
| 2nd Author's Affiliation | The University of Tokyo(UTokyo) |
| Date | 2022-06-09 |
| Paper # | NLP2022-7,CCS2022-7 |
| Volume (vol) | vol.122 |
| Number (no) | NLP-65,CCS-66 |
| Page | pp.pp.31-35(NLP), pp.31-35(CCS), |
| #Pages | 5 |
| Date of Issue | 2022-06-02 (NLP, CCS) |