PUBLICATIONS
PUBLICATIONS
H. Inoue, H. Tamura, A. Kitoh, X. Chen, Z. Byambadorj, T. Yajima, Y. Hotta, T. Iizuka, G. Tanaka, and I. Inoue,
Taming Prolonged Ionic Drift–Diffusion Dynamics for Brain-Inspired Computation,
Advanced Materials, vol. 37, no. 3, pp. 2407326 (2024).
H. Tamura and G. Tanaka,
Transfer-RLS Method and Transfer-FORCE Learning for Simple and Fast Training of Reservoir Computing Models,
Neural Networks, vol. 143, pp. 550-563 (2021).
H. Tamura, Y. Katori, and K. Aihara,
Possible Mechanism of Internal Visual Perception: Context-dependent Processing by Predictive Coding and Reservoir Computing Network,
Journal of Robotics, Networking and Artificial Life, vol. 6, no. 1, pp. 42-47 (2019).
H. Tamura, K. Fujiwara, K.Aihara, and G. Tanaka,
Online classification of multivariate time series data through Gaussian Reservoir State Analysis (GRSA),
Proc. 2025 International Joint Conference on Neural Networks (IJCNN, Rome, June 30 - July 5) (2025).
K. Nogami, H. Tamura, and G. Tanaka,
Federated learning with reservoir state analysis for time series anomaly detection,
Proc. 2025 International Joint Conference on Neural Networks (IJCNN, Rome, June 30 - July 5) (2025).
H.Tamura, K. Fujiwara, K. Aihara, and G. Tanaka,
Mahalanobis Distance of Reservoir States for Highly-Efficient Time-Series Classification,
Proc. the 33th Annual Conference of the Japanese Neural Network Society (JNNS, Tokyo, Sep. 4-6), pp. 49 (2023).
H. Inoue, H. Tamura, A. Kitoh, X. Chen, Z. Byambadorj, T. Yajima, Y. Hotta, T. Iizuka, G. Tanaka, and I. H. Inoue,
Long-Time-Constant Leaky-Integrating Oxygen-Vacancy Drift-Diffusion FET for Human-Interactive Spiking Reservoir Computing,
Proc. 2023 IEEE Symposium on VLSI Technology and Circuits (Kyoto, June 11-16), pp. 1-2 (2023).
H.Tamura, G.Tanaka, and K. Fujiwara,
Memory-Saving Time-Series Anomaly Detection Using Mahalanobis Distance of Reservoir States,
Proc. NEURO2022 (Ginowan, June 30 - July 3), pp. 533-534 (2022).
H. Tamura and G. Tanaka,
Partial-FORCE: a Fast and Robust Online Training Method for Recurrent Neural Networks,
Proc. 2021 International Joint Conference on Neural Networks (IJCNN, Virtual, July 18-22) (2021).
H. Tamura and G. Tanaka,
Extended full-FORCE method for training recurrent neural networks with fewer neurons,
Proc. the 30th Annual Conference of the Japanese Neural Network Society (JNNS, Virtual, Dec. 2-5), pp. 37-38 (2020).
H. Tamura and G. Tanaka,
Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing,
Proc. International Conference on Artificial Neural Networks (ICANN), pp. 459-469 (2020). (Best paper awarded)
H. Tamura, Y. Katori, and K. Aihara,
Possible Mechanism of Internal Visual Perception: Context-dependent Processing by Predictive Coding and Reservoir Computing Network,
Proc. International Conference on Artificial Life and Robotics (ICAROB, Beppu, Jan. 10-13) (2019).
S. Negishi, T. Hayami, H. Tamura, H. Mizutani, and H. Yamakawa,
Neocortical Functional Hierarchy Estimated from Connectomic Morphology in the Mouse Brain,
Proc. Ann. Int. Conf. Biologically Inspired Cognitive Architectures (BICA, Prague, Aug. 22-24), pp. 234-238 (2018).
H. Tamura, K. Fujiwara, K. Aihara, and G. Tanaka,
Distributional reservoir state analysis for real-time anomaly detection in multivariate time series data,
TechRxiv (2025).
H. Tamura, K. Fujiwara, K. Aihara, and G. Tanaka,
Mahalanobis Distance of Reservoir States for Online Time-Series Anomaly Detection,
TechRxiv (2023).
H. Tamura, H. Inoue, and T. Yajima,
Learning and Inference in Slow Electronics: Numerical Simulation,
Slow Electronics with Reservoir Computing, chap. 5, pp. 93-104 (2025).
H. Tamura,
Advanced Online Learning Methods for Recurrent Neural Networks,
Doctoral Thesis at The University of Tokyo (2022).
H. Tamura, T. Kohno, and K. Aihara,
ReLU Chaotic Neuron Model for Deep Chaotic Neural Networks,
SEISAN KENKYU, vol. 70, no. 3, pp. 183-185 (2018).