A Reconfigurable Architecture for Real-Time Prediction of Neural Activity

被引：0
|
作者：
Li, Will X. Y. [1 ]
Cheung, Ray C. C. [1 ]
Chan, Rosa H. M. [1 ]
Song, Dong [2 ]
Berger, Theodore W. [2 ]
机构：
[1] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[2] Univ So Calif, Dept Biomed Engn, Los Angeles, CA 90089 USA
来源：
2013 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2013年
关键词：
DYNAMICS; MODEL;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：1869 / 1872
页数：4
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