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.
Highlights
The combination of biological and artificial intelligence is 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
We ask whether the way biological organisms learn and make decisions could be altered by enhancing their brains with machine rule learning, and how applying the acquired rules by machines affects the learning ability of these hybrid systems
Six ratbots were used as the subjects to investigate three kinds of rule operations conducted by the computer integrated into the hybrid system, including rule learning, rule application, and rule combination and transfer:
Summary
The combination of biological and artificial intelligence is 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. Facilitated by current research in neural signal recording and processing[7,8] and micro stimulation[9], brains and machines are being interconnected with each other more tightly than ever, resulting in sensory[10], memory[11], and motor function rehabilitation or enhancement[11,12,13], animal robots[9,14,15,16,17,18] and cognitive robotics embodied with biological brains[19,20,21,22] Such a trend of biological and artificial intelligence integration engenders the question whether hybrid systems possess superior learning ability over their purely biological component. Six ratbots were used as the subjects to investigate three kinds of rule operations conducted by the computer integrated into the hybrid system, including rule learning, rule application, and rule combination and transfer:
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