Maze learning by a hybrid brain-computer system

Date

2016-09-13

Authors

Wu, Zhaohui
Zheng, Nenggan
Zhang, Shaowu
Zheng, Xiaoxiang
Gao, Liqiang
Su, Lijuan

Journal Title

Journal ISSN

Volume Title

Publisher

Nature Publishing Group

Abstract

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.

Description

Keywords

algorithms, animals, behavior, animal, brain, computer simulation, male, memory, neural networks (computer), rats, rats, sprague-dawley, stress, mechanical, artificial intelligence, brain-computer interfaces, maze learning

Citation

Source

Scientific reports

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

DOI

10.1038/srep31746

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