Personal technology affects every facet of modern life. We wake up to phone alarms, keep track of the day’s tasks on digital calendars, stay in contact with one another over social media, and preserve memories in photo libraries. The importance and ubiquity of personal devices suggest they can be considered a part of our cognitive processes. Our brains adapt to rely on external tools, integrating them into the greater “mind” even though they are materially separate [1]. As technology progresses, the line between mind and machine will only further blur.
Currently, for the brain to interact with a machine, it has to engage motor-modulation circuitry to coordinate physical movements and input information. It then has to engage either auditory or visual circuitry to perceive and process the machine’s output. These bottlenecks create great inefficiency in the flow of information. Imagine, instead, that machinery was integrated directly into neural circuitry, functioning as a frictionless extension of conscious experience. Cybernetics, also known as brain-machine interfaces, would allow engineers to implement additional cognitive capabilities. For example, one would no longer need to type in a destination to a navigation system and then follow directions - they would only need to trust their enhanced intuition. Cybernetics could also replace problematic brain circuitry. These devices therefore promise to alleviate debilitating diseases like Parkinson’s and paraplegia [2].
Research in such cybernetics is rapidly advancing. Traditionally, metal electrodes have either been fixed to the skull, the surface of the cortex, or inserted into brain tissue. However, these methods either lack resolution or are overly invasive. Diverse technologies are being developed to surpass these challenges.
Cal-Tech researchers recently made encouraging progress in developing a non-invasive neural monitoring technique. The researchers used ultrasound to measure minute changes in brain blood flow, which they found to be an effective proxy for neural activity. Achieving a resolution of 10 neurons in primate trials, the researchers were able to employ their device to accurately predict animal movements [3].
Other researchers are working on developing optogenetic stimulation systems. Through genetic modification, select neurons express proteins called “opsins” that are sensitive to a frequency of light. Upon exposure to that frequency, the opsins force the neuron to fire. By employing precise lasers, optogenetics provide a non-invasive way of transferring information into the brain [4].
Elon Musk’s Neuralink Corporation is the first organization to seriously contend delivering a full brain-machine interface. Their “sewing machine” system implements three innovations to great success. First, it utilizes ultrafine polymer probes, or “threads” that have greater flexibility and biocompatibility than traditional metal probes. Second, it uses a neurosurgical robot to rapidly sew in 96 threads around the brain, ensuring adequate coverage and resolution. The threads together have over 3000 electrodes. Last, the system places a processing disk right inside the skull, technology only made possible by recent advances in high density electronics [5]. Yet, the “sewing machine” approach still faces the challenge of dangerous open-brain surgery and the upkeep of thousands of delicate electrodes.
Although initial implementations are likely to be risky, expensive, and rudimentary, the pace of advancement is promising. After non-invasive monitoring and stimulation technologies are combined into a full interface, society will have to reconsider concepts of justice, personal responsibility, and free will. Cybernetics will revolutionize conscious experience; the possibilities of mental augmentation are endless and incredibly exciting.
References
- Smart, Paul. “Extended Cognition and the Internet: A Review of Current Issues and Controversies.” Philosophy & Technology, vol. 30, no. 3, Sept. 2017, pp. 357–90. DOI.org (Crossref), https://doi.org/10.1007/s13347-016-0250-2.
- Shih, Jerry J., et al. “Brain-Computer Interfaces in Medicine.” Mayo Clinic Proceedings, vol. 87, no. 3, Mar. 2012, pp. 268–79. DOI.org (Crossref), https://doi.org/10.1016/j.mayocp.2011.12.008.
- Norman, Sumner L., et al. “Single-Trial Decoding of Movement Intentions Using Functional Ultrasound Neuroimaging.” Neuron, vol. 109, no. 9, May 2021, pp. 1554-1566.e4. DOI.org (Crossref), https://doi.org/10.1016/j.neuron.2021.03.003.
- Yu, Chunxiu, et al. “Frequency-Specific Optogenetic Deep Brain Stimulation of Subthalamic Nucleus Improves Parkinsonian Motor Behaviors.” The Journal of Neuroscience, vol. 40, no. 22, May 2020, pp. 4323–34. DOI.org (Crossref), https://doi.org/10.1523/JNEUROSCI.3071-19.2020.
- Musk, Elon and Neuralink. “An Integrated Brain-Machine Interface Platform With Thousands of Channels.” Journal of Medical Internet Research, vol. 21, no. 10, Oct. 2019, p. e16194. DOI.org (Crossref), https://doi.org/10.2196/16194.