Huajin Tang

About

Huajin Tang is a prominent researcher at the intersection of computational neuroscience, neuromorphic computing, and autonomous robotics, with a particular focus on brain-inspired systems for spatial cognition and navigation. His most significant contributions center on developing neuro-inspired simultaneous localization and mapping (SLAM) frameworks that draw directly from the architecture of hippocampal place cells and entorhinal grid cells — biological structures known to underpin spatial memory in mammals. By translating these neural mechanisms into functional robotic systems, Tang has advanced how mobile robots perceive, map, and navigate complex real-world environments. His 2017 work on cognitive navigation integrating episodic memory and sequence learning has garnered 67 citations, establishing it as a foundational reference in the field. Alongside collaborators, he has pioneered RGB-D sensor-based cognitive mapping, direction-driven navigation, and deep learning approaches — including Restricted Boltzmann Machines and Deep Belief Networks — for autonomous object searching and behavior learning. His book *Neuromorphic Cognitive Systems* further reflects his commitment to synthesizing learning and memory principles into engineered intelligence. With cumulative citations exceeding 280 across his most notable works, Tang's research continues to shape the emerging discipline of brain-inspired robotics and neuromorphic cognitive architectures.

Research Focus

Key Achievements

Top Papers

1. 1
   Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory

   67 citations · 2017
2. 2
   Cognitive memory and mapping in a brain-like system for robotic navigation

   37 citations · 2016
3. 3
   An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building

   37 citations · 2015
4. 4
   RGB-D based cognitive map building and navigation

   35 citations · 2013
5. 5
   A brain-inspired SLAM system based on ORB features

   32 citations · 2017
6. 6
   Automatic Object Searching and Behavior Learning for Mobile Robots in Unstructured Environment by Deep Belief Networks

   20 citations · 2018
7. 7
   Direction-driven navigation using cognitive map for mobile robots

   20 citations · 2014
8. 8
   Real-Time Keypoint Recognition Using Restricted Boltzmann Machine

   14 citations · 2014
9. 9
   Neuromorphic Cognitive Systems A Learning and Memory Centered Approach

   12 citations · 2017
10. 10
    A Hybrid Loop Closure Detection Method Based on Brain-Inspired Models

    11 citations · 2022

Key Collaborators

Contact & Links