Yichen Jin

Papers

Total Citations

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About

Yichen Jin is a researcher at the intersection of robotics, artificial intelligence, and computational neuroscience, with a primary focus on bio-inspired navigation and perception systems. Their work is distinguished by pioneering the application of Hierarchical Temporal Memory (HTM)—a theory modeling the neocortex’s structure and algorithms—to robotic mapping and perception-action strategies. In their most cited work, "Simple perception-action strategy based on hierarchical temporal memory" (2013, 10 citations), Jin introduced a novel framework where HTM processes visual object inputs to enable robots to navigate indoor environments, effectively mimicking cortical reasoning. Building on this, their earlier paper "A novel mapping strategy based on neocortex model: Pre-liminary results by hierarchical temporal memory" (2012, 6 citations) proposed a map-building architecture inspired by the neocortex’s tree-shaped hierarchical structure, marking a significant departure from conventional SLAM methods. While their citation counts reflect an emerging rather than established impact, Jin’s contributions are notable for bridging cognitive neuroscience and practical robotics, offering a paradigm where robots perceive and act through biologically plausible learning. This work positions Jin as a forward-thinking contributor to neurorobotics, with potential to influence adaptive, brain-inspired autonomous systems.