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  1. Article ; Online: BRAND: a platform for closed-loop experiments with deep network models.

    Ali, Yahia H / Bodkin, Kevin / Rigotti-Thompson, Mattia / Patel, Kushant / Card, Nicholas S / Bhaduri, Bareesh / Nason-Tomaszewski, Samuel R / Mifsud, Domenick M / Hou, Xianda / Nicolas, Claire / Allcroft, Shane / Hochberg, Leigh R / Au Yong, Nicholas / Stavisky, Sergey D / Miller, Lee E / Brandman, David M / Pandarinath, Chethan

    Journal of neural engineering

    2024  Volume 21, Issue 2

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Neural Networks, Computer ; Brain-Computer Interfaces ; Neurosciences
    Language English
    Publishing date 2024-04-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2170901-4
    ISSN 1741-2552 ; 1741-2560
    ISSN (online) 1741-2552
    ISSN 1741-2560
    DOI 10.1088/1741-2552/ad3b3a
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6071: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  2. Article: BRAND: A platform for closed-loop experiments with deep network models.

    Ali, Yahia H / Bodkin, Kevin / Rigotti-Thompson, Mattia / Patel, Kushant / Card, Nicholas S / Bhaduri, Bareesh / Nason-Tomaszewski, Samuel R / Mifsud, Domenick M / Hou, Xianda / Nicolas, Claire / Allcroft, Shane / Hochberg, Leigh R / Yong, Nicholas Au / Stavisky, Sergey D / Miller, Lee E / Brandman, David M / Pandarinath, Chethan

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time ... ...

    Abstract Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed
    Language English
    Publishing date 2023-08-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.08.552473
    Database MEDical Literature Analysis and Retrieval System OnLINE

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