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- W2023246392 abstract "In this paper, we propose an FPGA-based hardware architecture for conducting real-time prediction of neural activity using a second-order generalized Laguerre-Volterra model (GLVM). This architecture serves as a rapid prototype of the prediction module of the future cognitive neural prosthetic device. We validate the functionality of the hardware model by utilizing the neuronal firing data of behaving rats trained to perform the delayed nonmatch-to-sample (DNMS) memory task." @default.
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- W2023246392 date "2013-05-01" @default.
- W2023246392 modified "2023-09-27" @default.
- W2023246392 title "A reconfigurable architecture for real-time prediction of neural activity" @default.
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- W2023246392 doi "https://doi.org/10.1109/iscas.2013.6572230" @default.
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