A Customizable Stochastic State Point Process Filter (SSPPF) for Neural Spiking Activity

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) proceedings
PublisherIEEE
Pages4993-4996
ISBN (Electronic)9781457702167
ISBN (Print)9781457702167
Publication statusPublished - Jul 2013

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Volume2013
ISSN (Print)1094-687X
ISSN (Electronic)1558-4615

Conference

Title35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC'13)
PlaceJapan
CityOsaka
Period3 - 7 July 2013

Abstract

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.

Citation Format(s)

A Customizable Stochastic State Point Process Filter (SSPPF) for Neural Spiking Activity. / Xin, Yao; Li, Will X.Y.; Min, Biao; Han, Yan; Cheung, Ray C.C.

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) proceedings. IEEE, 2013. p. 4993-4996 6610669 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vol. 2013).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review