Daisuke Uragami

According to our database, Daisuke Uragami authored at least 11 papers between 2012 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Generalization performance of reservoir computing implemented by asynchronously tuned elementary cellular automaton on parity task.

Kota Nakada, Kengo Takahashi, Daisuke Uragami, Ramón Alonso-Sanz, Andrew Adamatzky, Yuta Nishiyama

Artif. Life Robotics, February, 2026

2024

Social satisficing: Multi-agent reinforcement learning with satisficing agents.

Daisuke Uragami, Noriaki Sonota, Tatsuji Takahashi

Biosyst., 2024

2022

Universal Criticality in Reservoir Computing Using Asynchronous.

Daisuke Uragami, Yukio-Pegio Gunji

Complex Syst., 2022

2021

Computational Power of Asynchronously Tuned Automata Enhancing the Unfolded Edge of Chaos.

Yukio-Pegio Gunji, Daisuke Uragami

Entropy, 2021

2020

Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating.

Yukio-Pegio Gunji, Daisuke Uragami

Entropy, 2020

2018

Universal Emergence of 1/f Noise in Asynchronously Tuned Elementary Cellular Automata.

Daisuke Uragami, Yukio-Pegio Gunji

Complex Syst., 2018

2016

Robotic action acquisition with cognitive biases in coarse-grained state space.

Daisuke Uragami, Yu Kohno, Tatsuji Takahashi

Biosyst., 2016

2014

Cognitively inspired reinforcement learning architecture and its application to giant-swing motion control.

Daisuke Uragami, Tatsuji Takahashi, Yoshiki Matsuo

Biosyst., 2014

Multilayered neural network with structural lateral inhibition for incremental learning and conceptualization.

Daisuke Uragami, Hiroyuki Ohta

Biosyst., 2014

2012

Balancing between incremental learning and generalization by structured mutual inhibition: Shifting away from the question of whether there is a grandmother cell and toward conceptualization.

Daisuke Uragami, Hiroyuki Ohta

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012

Presynaptic inhibition balances the trade-off between differential sensitivity and reproducibility.

Hiroyuki Ohta, Daisuke Uragami, Yasuhiro Nishida, James C. Houk

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012