Yuichi Katori

Future University Hakodate, The University of Tokyo

Papers

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About

Yuichi Katori is a leading researcher in brain-inspired robotics and computational neuroscience, with a focus on integrating neural network models for autonomous systems. His key research areas include hippocampus-based navigation, reservoir computing, and reward-modulated learning for robot control. Katori’s most notable contribution is a brain-inspired neural network navigation system that models the hippocampus, prefrontal cortex, and amygdala, enabling robots to acquire spatial knowledge from minimal experiences in home environments—a breakthrough for efficient, biologically plausible AI. This work, published in 2021, has garnered 7 citations, reflecting its growing influence. He has also advanced short-term memory capabilities in reservoir-based temporal difference learning models (2022, 3 citations), demonstrating how recurrent neural networks can process complex time series with low computational cost for autonomous robot control. Additionally, Katori explored robot arm control using reward-modulated Hebbian learning (2021, 2 citations), bridging reinforcement learning and synaptic plasticity. His interdisciplinary approach, merging cognitive neuroscience with engineering, offers practical pathways for creating adaptive, memory-driven robots, making his work essential for students and researchers in neurorobotics and embodied AI.