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A parameter-optimization framework for neural decoding systems

Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine le...

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Autores principales: Xie, Jing, Chen, Rong, Bhattacharyya, Shuvra S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932190/
https://www.ncbi.nlm.nih.gov/pubmed/36817969
http://dx.doi.org/10.3389/fninf.2023.938689
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author Xie, Jing
Chen, Rong
Bhattacharyya, Shuvra S.
author_facet Xie, Jing
Chen, Rong
Bhattacharyya, Shuvra S.
author_sort Xie, Jing
collection PubMed
description Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models.
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spelling pubmed-99321902023-02-17 A parameter-optimization framework for neural decoding systems Xie, Jing Chen, Rong Bhattacharyya, Shuvra S. Front Neuroinform Neuroscience Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models. Frontiers Media S.A. 2023-02-02 /pmc/articles/PMC9932190/ /pubmed/36817969 http://dx.doi.org/10.3389/fninf.2023.938689 Text en Copyright © 2023 Xie, Chen and Bhattacharyya. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xie, Jing
Chen, Rong
Bhattacharyya, Shuvra S.
A parameter-optimization framework for neural decoding systems
title A parameter-optimization framework for neural decoding systems
title_full A parameter-optimization framework for neural decoding systems
title_fullStr A parameter-optimization framework for neural decoding systems
title_full_unstemmed A parameter-optimization framework for neural decoding systems
title_short A parameter-optimization framework for neural decoding systems
title_sort parameter-optimization framework for neural decoding systems
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932190/
https://www.ncbi.nlm.nih.gov/pubmed/36817969
http://dx.doi.org/10.3389/fninf.2023.938689
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