Maze learning by a hybrid brain-computer system

• Published in: Scientific reports Vol. 6; no. 1; p. 31746
• Main Authors: Wu, Zhaohui, Zheng, Nenggan, Zhang, Shaowu, Zheng, Xiaoxiang, Gao, Liqiang, Su, Lijuan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.09.2016; Nature Publishing Group
• Subjects: 639/166/985; 639/705/258; Algorithms; Artificial intelligence; Decision making; Humanities and Social Sciences; Maze learning; multidisciplinary; Rehabilitation; Science; Spatial discrimination learning; Spatial memory; Studies; Vibrissae
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep31746

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.