Neural growth into a microchannel network: towards a regenerative neural interface

P.A. Wieringa, Remy Wiertz, Jakob le Feber, Wim Rutten

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)
    61 Downloads (Pure)

    Abstract

    We propose and validated a design for a highly selective 'endcap' regenerative neural interface towards a neuroprosthesis. In vitro studies using rat cortical neurons determine if a branching microchannel structure can counter fasciculated growth and cause neurites to separte from one another, allowing for greater selective contact. Initial studies find that narrower branching microchannels achieve improved neurite separation. Electical stimulation of neurites within michrochannels is possible, as is recording of neurite action potentials with the microchannels acting as electrical singal amplifiers.
    Original languageUndefined
    Title of host publicationProceedings of the 4th International IEEE EMBS Conference on Neural Engineering
    Place of PublicationPiscataway
    PublisherIEEE EMBS
    Pages51-55
    Number of pages5
    ISBN (Print)978-1-4244-2073-5
    DOIs
    Publication statusPublished - 29 Apr 2009
    Event4th International IEEE/EMBS Conference on Neural Engineering, NER 2009 - Antalya, Turkey
    Duration: 29 Apr 20092 May 2009
    Conference number: 4

    Publication series

    Name
    PublisherIEEE-EMBS

    Conference

    Conference4th International IEEE/EMBS Conference on Neural Engineering, NER 2009
    Abbreviated titleNER
    CountryTurkey
    CityAntalya
    Period29/04/092/05/09

    Keywords

    • EWI-15361
    • BSS-Neurotechnology and cellular engineering
    • METIS-265206
    • IR-65493
    • neural growth michrochannel network neural interface

    Cite this

    Wieringa, P. A., Wiertz, R., le Feber, J., & Rutten, W. (2009). Neural growth into a microchannel network: towards a regenerative neural interface. In Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering (pp. 51-55). Piscataway: IEEE EMBS. https://doi.org/10.1109/NER.2009.5109232