Cyborg Rats and Monkey Mind Control

Cyborg intelligence relies on the meld of biology and silicon-based computing. It has the potential of enhancing our cognition, enhancing our perception, and enhancing our ability to actuate our thoughts. And … it isn’t just science fiction anymore.

Recent scientific breakthroughs have illuminated the directed information pathway from computer components to the brain and vice versa. These new findings have helped Gang Pan and his colleagues research the hierarchical and conceptual framework for cyborg intelligence.

“The term ‘cyborg intelligence’ was coined in a short article in IEEE Intelligent Systems magazine in 2013 and can be regarded as a new intelligence paradigm. Cyborg intelligence systems are a new kind of computational systems and need new development methodologies. With such a framework in place, the biological side could be considered as a set of application programming interfaces for development, putting it in the traditional programming paradigm. Its hierarchical design can help reduce complexity of development, and quicken the development of prototypes. It also can help to build a good integrated development environment (IDE),” says Pan.

“To understand neural representation in the brain, we explore encoding and decoding principles underlying the sensorimotor loop, and then computers implement novel AI algorithms to enhance sensation and motor control functions of the overall brain-computer integration system. Extensive experiments show that the concept and computation architecture of cyborg intelligence is promising for enhancing, repairing, or extending the intelligent capacity of both biological and computing units,” says Pan.

With their research, the team developed a series of rat cyborgs of different functions: vision-augmented, speech-augmented, learning-improved, decision-enhanced, and a group-movement team. “Most of them are the first prototype in the world,” says Pan.

“In the rat cyborgs, electrodes implanted in specific brain areas deliver electric stimuli. For vision-enhanced rat cyborg, a mini-camera mounted on the rat’s back captures movement or the surrounding environment. A computer analysis of the video stream generates stimulation parameters, which control the rat’s navigation behavior with virtual sensation or reward. With a speech-recognition interface, rat cyborg can navigate according to human words,” says Pan.

In addition to their work with the rat cyborgs, the team verified the brain-to-computer neural information path by implementing a “mind-reader” system. A monkey’s brain controlled four gestures of a robotic hand—grabbing, hooking, holding, and pinching.

“Biological systems possess all kinds of sensory abilities—vision, hearing, olfactory, haptic, and gustatory senses, to name a few. They also adapt to changes in external environments, and are capable of a range of cognitive functions. AI systems could greatly benefit from biological intelligence, solving problems that are still beyond the capabilities of the state of the art. For instance, image understanding is a relatively easy job for humans, yet it still challenges even the most sophisticated AI algorithms. I believe that brain-machine interfaces / neural interfaces are a promising way to help to achieve the goal. Recent years have seen quantum leaps in research dedicated to this linkage and the enormous potential enabled by deeply connecting and integrating biological and machine intelligence,” says Pan.

“Artificial intelligence can help people a lot, however the relationship between AI and human is loosely coupled, in the traditional sense of human-computer interaction. I believe human and machines/computers will become closely unified in the future, i.e. the biological intelligence and machine intelligence will be deeply integrated. Although AI cannot fully realize human intelligence in the near future, we could connect them and make them benefit from each other.”

Cyborg intelligence is an emerging direction in the field of intelligent systems, and Pan identifies a few important challenges in this area including:

  1. “real-time decoding and encoding of neural behaviors,
  2. regulation of biological neural circuits,  
  3. co-adaption and co-learning between biological and machine components,
  4. computational models for brain-in- loop intelligent systems, and
  5. biocompatible implanted Electronics.”

Additionally, Pan notes that society may not be ready to adapt all forms of cyborg intelligence.  “It needs time. Some applications of cyborg intelligence have been easily accepted, for example, neural prosthesis, while the whole concept of cyborg intelligence require more time to be accepted by the general public. Restoring human functions may be easier to accept, but it needs time to get augmentation of human functions accepted,” says Pan.  “The ethical issues require extensive discussions. The related research should be strictly approved.”

Read more in Cyborg Intelligence: Recent Progress and Future Directions.


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