TY - JOUR
T1 - Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory
AU - Tang, Huajin
AU - Yan, Rui
AU - Tan, Kay Chen
N1 - Funding Information:
Manuscript received May 19, 2017; revised September 30, 2017; accepted November 5, 2017. Date of publication November 23, 2017; date of current version September 7, 2018. This work was supported by the National Key Research and Development Program of China under Grant SQ2017YFB130092. (Corresponding author: Rui Yan.) H. Tang and R. Yan are with the Neuromorphic Computing Research Center, College of Computer Science, Sichuan University, Chengdu 610065, China (e-mail: [email protected]).
Publisher Copyright:
© 2016 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - One of the important topics in the study of robotic cognition is to enable robot to perceive, plan, and react to situations in a real-world environment. We present a novel angle on this subject, by integrating active navigation with sequence learning. We propose a neuro-inspired cognitive navigation model which integrates the cognitive mapping ability of entorhinal cortex (EC) and episodic memory ability of hippocampus to enable the robot to perform more versatile cognitive tasks. The EC layer is modeled by a 3-D continuous attractor network structure to build the map of the environment. The hippocampus is modeled by a recurrent spiking neural network to store and retrieve task-related information. The information between cognitive map and memory network are exchanged through respective encoding and decoding schemes. The cognitive system is applied on a mobile robot platform and the robot exploration, localization, and navigation are investigated. The robotic experiments demonstrate the effectiveness of the proposed system.
AB - One of the important topics in the study of robotic cognition is to enable robot to perceive, plan, and react to situations in a real-world environment. We present a novel angle on this subject, by integrating active navigation with sequence learning. We propose a neuro-inspired cognitive navigation model which integrates the cognitive mapping ability of entorhinal cortex (EC) and episodic memory ability of hippocampus to enable the robot to perform more versatile cognitive tasks. The EC layer is modeled by a 3-D continuous attractor network structure to build the map of the environment. The hippocampus is modeled by a recurrent spiking neural network to store and retrieve task-related information. The information between cognitive map and memory network are exchanged through respective encoding and decoding schemes. The cognitive system is applied on a mobile robot platform and the robot exploration, localization, and navigation are investigated. The robotic experiments demonstrate the effectiveness of the proposed system.
KW - Cognitive map
KW - cognitive navigation
KW - episodic memory
KW - neuromorphic cognitive systems
KW - simultaneously localization and mapping (SLAM)
UR - http://www.scopus.com/inward/record.url?scp=85035788410&partnerID=8YFLogxK
U2 - 10.1109/TCDS.2017.2776965
DO - 10.1109/TCDS.2017.2776965
M3 - Journal article
AN - SCOPUS:85035788410
SN - 2379-8920
VL - 10
SP - 751
EP - 761
JO - IEEE Transactions on Cognitive and Developmental Systems
JF - IEEE Transactions on Cognitive and Developmental Systems
IS - 3
M1 - 8119542
ER -