Cited by 91Neuroscience and Neural EngineeringMolecular Communication and NanonetworksEEG and Brain-Computer Interfaces

The DOI matches its Crossref record; 246 of the 247 references it cites were found in a registry or an index; 57 of the 175 sources its claims cite were checked for support.

Human Brain/Cloud Interface

Open access

Abstract

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper proposes a human brain/cloud interface (B/CI) enabled by neuralnanorobots that would monitor and transmit brain activity to cloud supercomputers. It argues this could provide real-time access to cumulative human knowledge and enable applications such as transparent shadowing, while acknowledging the technology is speculative and 20–30 years away.

Key findings

  • Three neuralnanorobot classes are proposed: endoneurobots at axon initial segments, gliabots within glial cells, and synaptobots near synapses, to monitor the brain’s ∼86 × 10⁹ neurons, ∼2 × 10¹⁴ synapses, and ∼84 × 10⁹ glial cells.
  • The brain is estimated to process ∼5.52 × 10¹⁶ bits/sec via ∼4.31 × 10¹⁵ spikes/sec; a proposed 30 cm³ nanorobotic fiber-optic system could handle up to 10¹⁸ bits/sec for cloud transfer.
  • Neuralnanorobotics is claimed to potentially treat ∼400 brain disorders and enable B/CI applications including education, intelligence enhancement, VR/AR, and “transparent shadowing”; a three-human BrainNet BTBI achieved average accuracy of 0.813.
  • Volume displacement estimates: endoneurobots plus gliabots would occupy ∼0.12% of brain volume, while synaptobots would occupy ∼0.9–9%, described as just within a 1–10% “safe” intrusiveness limit.

Methods

  • Conceptual/review article, not an empirical study; no new experimental data, human subjects, or clinical sample are reported.
  • Synthesizes prior literature and engineering estimates—neuron/synapse counts, data rates, volumes, latency, and biocompatibility—to argue feasibility of a neuralnanorobotic B/CI.
  • Proposes an architecture and protocols for nanorobot ingress, blood–brain barrier traversal, synaptic/neuronal monitoring, data transmission, biocompatibility, and FDA/ethics review.

Limitations

  • Stated: further study is required to quantify the population, dimensions, and distribution of naturally occurring BBB perijunctional gaps; a comprehensive ethical analysis is expected before implementation; some display modes are beyond the paper’s scope.
  • Evident: no experimental validation of neuralnanorobots is presented; the proposal depends on future technologies such as diamondoid nanofactories, advanced 5G, and cloud supercomputers.
  • Evident: safety, efficacy, and biocompatibility claims are projections rather than demonstrated outcomes, and the 20–30-year timeline is speculative.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fnins.2019.00112

Cites

246 of 247 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

57 of 175 cited sources checked · 33 supported · 0 flagged · accuracy not yet measured

Show all 69 claims

  • Probably not a substantial claim. 117
  • It may state two separate claims as one. 67
147 of 147
  • The paper relies on this without support.

    The human brain contains approximately 86 × 10^9 neurons and 2 × 10^14 synapses.

    “More specifically, endoneurobots are autonomous neuron-resident neuralnanorobots that interface with all ∼86 × 109 human–brain neurons at the AIS to directly monitor and interact with action-potential-based electrically processed information.”
  • The paper relies on this without support.

    Future human B/CI technologies require long-term, self-implanting in vivo neural interface systems.

    “Future human B/CI technologies may preferably require long-term, self-implanting in vivo neural interface systems, a characteristic that is absent from most current BMI technologies.”
  • The paper relies on this without support.

    Human knowledge is being digitized at an accelerated exponential pace for storage and processing in the cloud.

    “Human knowledge is being digitized at an accelerated exponential pace for storage and processing in the cloud.”
  • The paper relies on this without support.

    Neuralnanorobotics may provide a technology at the appropriate scale, with a suitable level of complexity to robustly interface the human brain with the massive volume of data that is stored and processed in the cloud.

    “Neuralnanorobotics may provide a technology at the appropriate scale, with a suitable level of complexity to robustly interface the human brain with the massive volume of data that is stored and processed in the cloud.”
4 of 4

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

  • Brain-component percentages do not match the stated intracranial volume

    Minor

    The listed volumes total 1,700 cm3, but 1,350 cm3 is about 79.4% of that total, not 75%. The listed blood and cerebrospinal-fluid percentages also do not match their volumes.

    Reported 75 · recomputed 79.411765

  • Occipital synapse count implies more neurons than the table allows

    Minor

    Table 2's 22.0 trillion occipital synapses divided by 4.36 thousand synapses per neuron implies about 5.05 billion neurons. That exceeds the listed upper bound of 4.65 billion.

    Reported 4.65 · recomputed 5.045872

2 of 2

Citations

Cites

Items 201–247 of 247

Flagged first, then by citations

Research works printed but not linked: 34

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  6. Domschke, 2014, Quandary - Are Molecularly Manufactured Burgers Imbued with the Life Force? (Fqxi Essay Contest, in Response to the Question, How Should Humanity Steer the Future?)
  7. Finley, 2018, The Wired Guide to 5G.
  8. Ford, 2010, The Secrets of Intelligence Lie within a Single Cell: New Scientist, The Big Idea.21 April.
  9. Freitas, Is Diamond Biocompatible With Living Cells?
  10. Freitas, 2002, Is Sapphire Biocompatible With Living Cells?
  11. Freitas, Current status of nanomedicine and medical nanorobotics (Invited Survey)., J. Comput. Theor. Nanosci., 2, 1
  12. Freitas, 2016, The Alzheimer Protocols: A Nanorobotic Cure for Alzheimer’s Disease and Related Neurodegenerative Conditions.
  13. Freitas, 2006, Nanofactory Collaboration.
  14. Goertzel, 2006, The Hidden Pattern.
  15. Harris, 1999, Synapse Web.
  16. 2008, IBM Seeks to Build the Computer of the Future Based on Insights from the Brain IBM Awarded, D. A.RPA Funding for Cognitive Computing Collaboration.
  17. Kemp, 2018, The State of the Internet in Q4. We Are Social.
  18. Knapp, 2013, Chinese Supercomputer Is Now The World’s Fastest - By A Lot.
  19. Kurzweil, 2014, Get Ready for Hybrid Thinking. Ted Talk.
  20. Martins, 2015, Action potential monitoring using neuronanorobotics: neuroelectric nanosensors., Intl. J. Nanomaterials and Nanostructures, 1, 20
  21. Mavroides, 2011, Nanorobotics: Current Approaches and Techniques.
  22. Merkle, 1989, Energy Limits to the Computational Power of the Human Brain.
  23. Minsky, 1985, The Society of Mind.
  24. 2017, National Institute of Neurological Disorders and Stroke.
  25. 2019, Open Brain Computer Interface.
  26. Saha, 2008, Vertically Arranged Gold Nanowires: An Interface for Live Neuronal Recordings., 1
  27. Sandberg, 2008, Whole Brain Emulation: A Roadmap.
  28. Schneider, 2009, Science Fiction and Philosophy: From Time Travel to Superintelligence
  29. 2018, Data Center Storage Capacity Worldwide from 2016 to 2021 by Segment (in Exabytes).
  30. Tweedie, 2015, How Do You Move In Virtual Reality? With a Treadmill Like This One I Just Tried.
  31. 2016, Thirty-Second Session, Agenda Item 3: Promotion and Protection of All Human Rights, Civil, Political, Economic, Social and Cultural Rights, Including the Right to Development.
  32. 2014, IP Latency Statistics, Verizon.
  33. Whitby, 2000, How to avoid a robot takeover: political and ethical choices in the design and introduction of intelligent artifacts., Presented presented at the AISB-00 Symposium on Artificial Intelligence, Ethics an (Quasi-) Human Rights
  34. Zigmond, 2014, Neurobiology of Brain Disorders: Biological Basis of Neurological and Psychiatric Disorders.

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Metadata

Authors
Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., Hogg, T., & Freitas, R. A.
Affiliations
  • Lawrence Berkeley National Laboratory (US)
  • UC San Diego Health System (US)
  • National Research University Higher School of Economics (RU)
  • Purdue University West Lafayette (US)
  • Uniformed Services University of the Health Sciences (US)
  • Institute for Molecular Manufacturing (US)
Corresponding author
Martins, Nuno R. B.
Venue
Frontiers in Neuroscience
Published
2019-03-29
Volume
13
Pages
112-112
ISSN
1662-453X (ISSN-L)
OpenAlex
W2927435888
Cited by
91
References
242
Citations by year
2026: 4 · 2025: 9 · 2024: 13 · 2023: 9 · 2022: 16 · 2021: 28 · 2020: 8 · 2019: 4
FWCI
5.85
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Research University Higher School of Economics (14.641.31.0003)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Physical Sciences › Engineering › Biomedical Engineering › Molecular Communication and Nanonetworks
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
blood-brain barrier, axon initial segment, glial cells, synapses, cognitive disorders, neuromonitoring, cloud computing

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