IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v47y2016icp49-59.html
   My bibliography  Save this article

Becoming a network beyond boundaries: Brain-Machine Interfaces (BMIs) as the actor-networks after the internet of things

Author

Listed:
  • Ahn, Sungyong

Abstract

For the last few decades, actor-network theory has been usually criticized for its focus on Machiavellian human actors controlling the overall networking between other actors or blamed for its dissipation of human agencies within the global status of a network. However, at the moment of its development, the freshness of actor-network theory was more relevant to its focus on the meaning of what the hyphen signifies; not so much just a simple connection between two independent variables, but an ontological event itself, from which certain entities juxtaposed together become involved by exchanging stable influences each other thus settled down as the actors participating in a network being associated as the summing up of these settled influences. The aim of this paper is to refresh this bygone freshness of ANT from the recent development of the Internet of Things (IoT); which vividly exemplifies how a network and its actors are generated from a manifold technologically augmented entities—such as smart appliances in a house, migratory animals with RFID tags, and ensembles of neurons signaling beyond one's brain through a bundle of microwires—each of which is physiologically or algorithmically adaptable to the environmental signals from other entities thus able to be settled down together into “new sensor/processor/actuator affiliations.” Brain-Machine Interface (BMI), developed by Nicolelis Lab at Duke University as a prototype of the future neuroprosthetics, shows a specific example of these networks of things; in which mutual adaptations of the technologically augmented entities—namely neurons and robot limbs—associate artificial sensory-motor circuits, programming its human/animal users' possible motor behaviors as well as their motor intentions.

Suggested Citation

  • Ahn, Sungyong, 2016. "Becoming a network beyond boundaries: Brain-Machine Interfaces (BMIs) as the actor-networks after the internet of things," Technology in Society, Elsevier, vol. 47(C), pages 49-59.
  • Handle: RePEc:eee:teinso:v:47:y:2016:i:c:p:49-59
    DOI: 10.1016/j.techsoc.2016.08.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X16300926
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2016.08.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zheng Li & Joseph E O'Doherty & Timothy L Hanson & Mikhail A Lebedev & Craig S Henriquez & Miguel A L Nicolelis, 2009. "Unscented Kalman Filter for Brain-Machine Interfaces," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu Zhu & Jian-an Fang & Yang Tang & Wenbing Zhang & Wei Du, 2012. "Digital IIR Filters Design Using Differential Evolution Algorithm with a Controllable Probabilistic Population Size," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    2. Rodina, L. & Harris, L. & Ziervogel, G. & Wilson, J., 2024. "Resilience counter-currents: Water infrastructures, informality, and inequities in Cape Town, South Africa," World Development, Elsevier, vol. 180(C).
    3. Andrey Eliseyev & Tetiana Aksenova, 2016. "Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    4. Josh Merel & David Carlson & Liam Paninski & John P Cunningham, 2016. "Neuroprosthetic Decoder Training as Imitation Learning," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-24, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:47:y:2016:i:c:p:49-59. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.