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Hypothesis and Theory ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00112

HUMAN BRAIN/CLOUD INTERFACE

  • 1Lawrence Berkeley National Laboratory, United States Department of Energy (DOE), United States
  • 2University of California, Berkeley, United States
  • 3KurzweilAINetwork, Inc., Newton, MA, USA, United States of America
  • 4UC San Diego Health, United States
  • 5VA San Diego Healthcare System, United States
  • 6Nanobot Medical Animation Studio, San Diego, CA, USA, United States
  • 7NanoApps Medical, Inc.,, Canada
  • 8University of Miami, United States
  • 9Department of Neurobiology, School of Medicine, Duke University, United States
  • 10Purdue University, United States
  • 11Monash University, Australia
  • 12Institute for Molecular Manufacturing, United States

The internet comprises a decentralized global system that serves humanity’s collective effort to generate, process, and store information. A significant portion of this data is currently processed and stored in the cloud, and the last few years have witnessed the doubling of cloud storage and cloud computing. Given such tremendous growth there is an urgent need to create a stable, secure, and continuous real-time system for interfacing the human brain with the cloud. One promising strategy for the creation of human brain/cloud interface (B/CI) technologies may be referred to as “neuralnanorobotics.”
Neuralnanorobotic technology might well emerge from efforts to comprehensively address cognitive disorders and is expected to facilitate the accurate diagnoses and eventual cures for the ~400 conditions that affect the human brain. Neuralnanorobotics is also anticipated to have numerous non-medical applications -- for example, serving as a platform to enable a robust, ultrahigh-resolution interface between the human brain and the cloud. Neuralnanorobotics may permit a safe, secure, and stable real-time whole-human brain interface with the cloud, allowing for controlled connectivity between neural activity and external data storage and data processing. This strategy may involve direct and comprehensive monitoring of the estimated ~86 x 109 neurons of the human brain, along with its estimated ~2 x 1014 synapses, through the use of nanometric medical nanorobotics. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) would traverse the blood-brain barrier, enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to every synapse or group of synapses (synaptobots).
A neuralnanorobotically assisted human B/CI is expected to give rise to an extensive panoply of applications, among which would be the application of serving as a personalized conduit for individuals to obtain direct, instantaneous access to any facet of cumulative human knowledge available in the cloud. Other anticipated applications include a myriad of opportunities for improving education, intelligence, entertainment, traveling, and other interactive experiences.

Keywords: Brain/Cloud Interface , Brain-computer interface, Brain-to-brain interface, brain-machine interface, Transparent Shadowing, Neuralnanorobots, Neuralnanorobotics, Medical nanorobots, Nanorobots; Nanomedicine, Nanotechnology

Received: 10 Sep 2018; Accepted: 30 Jan 2019.

Edited by:

Hari S. Sharma, Uppsala University, Sweden

Reviewed by:

Vassiliy Tsytsarev, University of Maryland, College Park, United States
Brent Winslow, Design Interactive (United States), United States  

Copyright: © 2019 Martins, Angelica, Chakravarthy, Svidinenko, Boehm, Opris, Lebedev, Swan, Rosenfeld, Garan, Hogg and Freitas Jr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Nuno R. Martins, Lawrence Berkeley National Laboratory, United States Department of Energy (DOE), Berkeley, California, United States, nunorbmartins@gmail.com