Xin Yao, Li Will X Y, Min Biao, Han Yan, Cheung Ray C C
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4993-6. doi: 10.1109/EMBC.2013.6610669.
Stochastic State Point Process Filter (SSPPF) is effective for adaptive signal processing. In particular, it has been successfully applied to neural signal coding/decoding in recent years. Recent work has proven its efficiency in non-parametric coefficients tracking in modeling of mammal nervous system. However, existing SSPPF has only been realized in commercial software platforms which limit their computational capability. In this paper, the first hardware architecture of SSPPF has been designed and successfully implemented on field-programmable gate array (FPGA), proving a more efficient means for coefficient tracking in a well-established generalized Laguerre-Volterra model for mammalian hippocampal spiking activity research. By exploring the intrinsic parallelism of the FPGA, the proposed architecture is able to process matrices or vectors with random size, and is efficiently scalable. Experimental result shows its superior performance comparing to the software implementation, while maintaining the numerical precision. This architecture can also be potentially utilized in the future hippocampal cognitive neural prosthesis design.
随机状态点过程滤波器（SSPPF）在自适应信号处理中很有效。特别是，近年来它已成功应用于神经信号编码/解码。最近的工作证明了其在哺乳动物神经系统建模中的非参数系数跟踪方面的效率。然而，现有的SSPPF仅在商业软件平台中实现，这限制了它们的计算能力。本文设计了SSPPF的首个硬件架构，并在现场可编程门阵列（FPGA）上成功实现，为在成熟的广义拉盖尔 - 沃尔泰拉模型中进行系数跟踪提供了一种更有效的方法，用于哺乳动物海马体尖峰活动研究。通过探索FPGA的内在并行性，所提出的架构能够处理任意大小的矩阵或向量，并且具有高效的可扩展性。实验结果表明，与软件实现相比，它具有卓越的性能，同时保持了数值精度。这种架构未来也可能用于海马体认知神经假体设计。
