BRAND: a platform for closed-loop experiments with deep network models
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Le résumé fourni par la source
Abstract Objective. 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++). Approach. To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes , which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. Main results. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. Significance. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- BRAND: a platform for closed-loop experiments with deep network models
- Date Crossref
- 01/04/2024
- Éditeur
- IOP Publishing
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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The Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Northwestern University Department of Neuroscience pays non établi dans la noticeUniversité ou école supérieure
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Neurological Surgery pays non établi dans la noticeÉtablissement de santé
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University of California Department of Neurological Surgery pays non établi dans la noticeUniversité ou école supérieure
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Emory University Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Massachusetts General Hospital Department of Neurology pays non établi dans la noticeÉtablissement de santé
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Center for Neuro-Oncology pays non établi dans la noticeStructure de recherche
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Allen Institute for Brain Science pays non établi dans la noticeStructure de recherche
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Brown University pays non établi dans la noticeUniversité ou école supérieure
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Providence VA Medical Center Veterans Affairs Rehabilitation Research & Development Center for Neurorestoration and Neurotechnology pays non établi dans la noticeÉtablissement de santé
Wallace H. Coulter Department of Biomedical Engineering — Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering et Department of Neuroscience — Northwestern University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.