Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China.
IEEE Trans Biomed Circuits Syst. 2013 Aug;7(4):489-98. doi: 10.1109/TBCAS.2012.2228261.
A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients. Test results show that compared with previous software implementation of the generalized Laguerre-Volterra algorithm running on an Intel Core i7-2620M CPU, the FPGA-based hardware system can achieve up to 2.66×10(3) speedup in doing model parameters estimation and 698.84 speedup in doing model output prediction. The proposed hardware platform will facilitate research on the highly nonlinear neural process of the mammal brain, and the cognitive neural prosthesis design.
提出并利用基于现场可编程门阵列（FPGA）的硬件架构来预测神经元群体的放电活动。硬件系统采用多输入多输出（MIMO）广义勒让德-沃尔泰拉模型（GLVM）结构来描述哺乳动物大脑的非线性动态神经过程，并且可以在两个重要功能之间切换：GLVM 系数的估计和神经元群体尖峰活动的预测（模型输出）。首先使用样本内训练数据来估计模型系数；然后使用样本外测试数据和现场估计系数来预测输出。测试结果表明，与在 Intel Core i7-2620M CPU 上运行的先前的广义勒让德-沃尔泰拉算法的软件实现相比，基于 FPGA 的硬件系统在进行模型参数估计时可以实现高达 2.66×10(3)的加速，在进行模型输出预测时可以实现高达 698.84 的加速。所提出的硬件平台将有助于研究哺乳动物大脑的高度非线性神经过程和认知神经假体设计。
