Abstract Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.

Citation: Yu Y, Pan G, Gong Y, Xu K, Zheng N, Hua W, et al. (2016) Intelligence-Augmented Rat Cyborgs in Maze Solving. PLoS ONE 11(2): e0147754. https://doi.org/10.1371/journal.pone.0147754 Editor: Nanyin Zhang, Penn State University, UNITED STATES Received: June 18, 2015; Accepted: January 7, 2016; Published: February 9, 2016 Copyright: © 2016 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This work has been supported by National Key Basic Research Program of China (2013CB329504) (http://www.973.gov.cn/English/Index.aspx), Zhejiang Provincial Natural Science Foundation of China (LR15F020001) (http://www.zjnsf.gov.cn/), and Program for New Century Excellent Talents in University (NCET-13-0521) (http://www.moe.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Within the past two decades, bio-robots have been realized on different kinds of creatures, such as cockroaches [1], moths [2], beetles [3], and rats [4–7]. They are expected to be superior to traditional mechanical robots in mobility, perceptivity, adaptability, and energy consumption [8–11]. Among them, rat robots are becoming popular for their good maneuverability. To make a rat robot, a pair of micro electrodes are implanted into the medial forebrain bundle (MFB) of the rat’s brain, and the other two pairs are implanted into the whisker barrel fields of left and right somatosensory cortices (SI). After the rat recovers from the surgery, a wireless micro-stimulator is mounted on the back of the rat to deliver electric stimuli into the brain through the implanted electrodes. This allows a user, using a computer, to deliver stimulus pulses to any of the implanted brain sites remotely. Stimulation in MFB can excite the rat robot by increasing the level of dopamine in its brain, and stimulation in the left or right SI makes the rat robot feel as if its whiskers are touching a barrier. Before a rat robot is used for navigation, a training process is usually needed to reinforce the desired behaviors (i.e. moving ahead, turning left and turning right). In this process, the MFB stimulation acts as a reward as well as a cue to move ahead, and the left and right SI stimulation act as the cues to turn left and turn right respectively. In order to get the reward, the rat robot will learn to do the correct behaviors corresponding to the cues. The references [4, 12, 13] provide more details. After sufficient navigation training, the rat robot will move ahead in response to the Forward cue, turn left in response to the Left cue, and turn right in response to the Right cue. In this paper, the rat robot is referred to as rat cyborg. One of the reasons that researchers take interest in developing rat cyborgs is that rats have outstanding spatial localization abilities. They can find a way in an environment by orienting themselves in relation to a wide variety of cues, including distal cues, typically provided by vision, audition, and olfaction, as well as proximal cues, typically provided by tactile, kinesthetic, and inertial systems [14]. Rats even have a well-developed magnetic compass sense for spatial orientation [15–17]. Rats do not build detailed geometrical representations of the environment. They rely on the learnt associations between external perception and the pose belief created from the self-motion cues. Studies of spatial orientation posit that rats use an “inner GPS” in hippocampus and entorhinal cortex to create a cognitive map of the environment [18–22]. Furthermore, RatSLAM, a navigation model which is inspired by rat’s brain, can perform simultaneous localization and mapping in real time on a mechanical robot [23–25]. On the other hand, machines (or computers) are efficient in numerical computation, information retrieval, statistical reasoning, and have almost unlimited storage. Different to rats, machines have their own methods to explore and learn about the environment in spatial navigation problems. They can capture many categories of information from the environment through various sensors, such as range sensors, visual sensors, vibration sensors, acoustic sensors, and location sensors. The information is saved and converted into a discrete and digital form. Then the processed information will be synthesized to map the environment by different paradigms [26, 27]. Eventually, machines can choose various searching algorithms in path planning, for example, flood-fill method, Dijkstra’s algorithm, A* search algorithm, rapidly exploring random tree, probabilistic roadmap. Machines run the sense process, map process, and decision process in real time and in parallel. A typical demonstration of machine’s navigation abilities is the micromouse competition, in which a small rat-like mechanical robot explores a 16×16 maze [28]. For a specific application, biological creatures and machines both have their own strengths and weaknesses. In this paper, we ask the question: can biological intelligence be augmented with the help of machine intelligence? To explore the question, a maze solving task was introduced, in which three kinds of subjects (computer, rats and rat cyborgs) were asked to find their ways from a predetermined starting position to a predetermined target position. It is a challenge to embed the machine’s maze solving capability into their navigation. In our experiments, the computer traversed the mazes based on an improved wall follower approach, six rats traversed 14 mazes one by one all by themselves, and then traversed the 14 mazes again in the same order with the assistance of the computer. Performance was measured by steps, coverage rates, and time spent, allowing for comparisons.

Discussion The experimental results show that, in terms of steps, coverage rates and time spent, rat cyborgs performed better than rats in maze solving. However, in terms of steps, performance of rat cyborgs did not show remarkable advantages over that of computer. Actually, rats did not strive to reach the destination in the least number of steps. They sometimes would revisit visited cells again and again, and wander between adjacent cells. Nonetheless, all of the six rat cyborgs outperformed the computer in terms of the coverage rates. Moreover, the rat cyborgs are agile in different types of terrain (see S1 Video), and have the potential to solve unanticipated problems relying on instinct [38]. Because the six rats and the six rat cyborgs were the same rats, the possible interference should be carefully prevented. In our experiments, we took four measures to avoid interference: (1) for a rat and its rat cyborg, the layout of each maze was the same, while the physical walls of each maze cell were changed; (2) a maze solved by a rat would be solved by its rat cyborg at least 4 days later, not in the following day; (3) in the interval, the rat would be asked to carry out experiments with other mazes to further weaken its memory of the previous maze; (4) between the procedure of maze solving by rats and the procedure of maze solving by rat cyborgs, the entire maze was washed and dried to remove the possible odor interference. In order to verify that the performance enhancement of the rat cyborgs in maze solving was not attributed to what the rats had experienced, we conducted two supplemental experiments. In supplemental experiment 1, two rats (i.e. M01 and M03) first traversed 5 mazes (maze 15 to maze 19, see S1 Fig) one by one with the assistance of the computer, and then traversed the 5 mazes again in the same order all by themselves. If a strong memory of the mazes has been gained by the rat cyborgs, it will then benefit the performance of the rats. The experimental results are shown in Fig 9. As we can see, the steps of each rat cyborg are less than that of the corresponding rat in each maze. The coverage rates of each rat cyborg are less than that of the corresponding rat except in maze 17 of M03, the coverage rates of each rat cyborg are less than that of the computer except in maze 18, and the average coverage rates of each rat cyborg (M01: 49.40±7.72, M03: 49.20±7.51) are less than that of the corresponding rat(M01: 59.80±9.68, M03: 56.60±8.35) and the computer (60.00±2.57). Besides, M01 spent less time in maze solving with the assistance of the computer except in maze 17. These results show that rat cyborgs have a better performance than rats. This is consistent with the conclusion of the previous experiments, and demonstrates that the performance enhancement of the rat cyborgs in maze solving should not be attributed to what the rats had experienced. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 9. Steps, coverage rates and time spent of maze solving by computer, M01 and M03 in maze 15 to maze 19. (a) Steps of maze solving by computer and M01. (b) Steps of maze solving by computer and M03. (c) Coverage rates of maze solving by computer and M01. (d) Coverage rates of maze solving by computer and M03. (e) Time spent of maze solving by computer and M01. (f) Time spent of maze solving by computer and M03. https://doi.org/10.1371/journal.pone.0147754.g009 In supplemental experiment 2, two rats (i.e. M01 and M03) first traversed other 5 mazes (maze 20 to maze 24) one by one all by themselves. Then these 5 mazes were flipped along the diagonal axis from the entrance to the exit, and traversed by the two rats in the same order with the assistance of the computer. Note that each pair of the flipped maze and the original one (see S1 Fig) have the same complexity but different space layouts. In this way, a rat’s memory of the original maze can hardly help the counterpart rat cyborg solve the flipped maze. The experimental results are shown in Fig 10. As we can see, the steps of each rat cyborg are less than that of the corresponding rat in each maze. The coverage rates of each rat cyborg are less than that of the computer except in maze 24 of M01, and the average coverage rates of each rat cyborg (M01: 46.00±8.92, M03: 36.40±3.36) are less than that of the corresponding rat(M01: 52.40±7.52, M03: 40.80±7.22) and the computer (56.60±2.96). Besides, M03 spent less time in maze solving with the assistance of the computer except in maze 21. These results show that rat cyborgs have a better performance than rats. This is consistent with the conclusion of the previous experiments, and also demonstrates that the performance enhancement of the rat cyborgs in maze solving should not be attributed to what the rats had experienced. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 10. Steps, coverage rates and time spent of maze solving by computer, M01 and M03 in maze 20 to maze 24. (a) Steps of maze solving by computer and M01. (b) Steps of maze solving by computer and M03. (c) Coverage rates of maze solving by computer and M01. (d) Coverage rates of maze solving by computer and M03. (e) Time spent of maze solving by computer and M01. (f) Time spent of maze solving by computer and M03. https://doi.org/10.1371/journal.pone.0147754.g010 Thanks to the rapid advance of brain-machine interfaces (BMIs), the connection and interaction between the organic components and computing components of the cyborg intelligent systems are becoming deeper and better [39–43]. Based on such kinds of symbiotic bio-machine systems, a new type of intelligence, which we refer to as cyborg intelligence, will play an increasingly crucial role. Cyborg intelligence is a convergence of machine and biological intelligence, which is capable of integrating the two heterogeneous intelligences at multiple levels [44, 45]. It has the potential to deliver tremendous benefits to society, such as in search and rescue, health care, and entertainment. In this work, the mobility, perceptibility and cognition capability of the rats were combined with the sensing and computing power of the machines in the rat cyborg system. The experimental results of the rat cyborg system in maze solving provide a proof-of-principle demonstration for cyborg intelligence.

Conclusions and Future Work In this paper, we build intelligence-augmented rat cyborgs and present a comparative study of maze solving by computer, by rats, and by rat cyborgs. Computer aids rats in dead road detection, unique road detection, loop detection, and shortest path detection. In terms of steps, coverage rates and time spent, the rat cyborgs have a better performance than the individual rats in maze solving; in terms of coverage rates, the rat cyborgs have a better performance than the individual computer in maze solving. From the systematic perspective, the rat’s capability of maze solving has been augmented by the computer. In future work, more tasks will be introduced, and the complexity of tasks will be quantified. To avoid excessive intervention with the rats, the strength of the computer’s assistance will be graded. In addition, more practical rat cyborgs will be investigated: the web camera will be replaced by sensors mounted on rats, such as tiny camera, ultrasonic sensors, infrared sensors, electric compass, and so on, to perceive the real unknown environment in real time; and the computer-aided algorithms can be housed on a wireless backpack stimulator instead of in the computer.

Acknowledgments This work is supported by National Key Basic Research Program of China (2013CB329504), Zhejiang Provincial Natural Science Foundation of China (LR15F020001), and Program for New Century Excellent Talents in University (NCET-13-0521). The authors are grateful to the editor and reviewers for their insightful comments. They also are pleased to thank Liqiang Gao, Liujing Zhuang, Shengzhang Lai for technical support and Chaonan Yu for the rat surgery.

Author Contributions Conceived and designed the experiments: YY GP KX ZW. Performed the experiments: YY WH YG. Analyzed the data: YY GP YG. Contributed reagents/materials/analysis tools: YY GP KX NZ XZ. Wrote the paper: YY GP.