A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities

Abstract

A parallelized and pipelined architecture based on FPGA and a higher-level Self Reconfiguration Platform are proposed in this paper to model Generalized Laguerre-Volterra MIMO system essential in identifying the time-varying neural dynamics underlying spike activities. Our proposed design is based on the Xilinx Virtex-6 FPGA platform and the processing core can produce data samples at a speed of 1.33 × 10(6)/s, which is 3.1 × 10(3) times faster than the corresponding C model running on an Intel i7-860 Quad Core Processor. The ongoing work of the construction of the advanced Self Reconfiguration Platform is presented and initial test results are provided.