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

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 1.33×106/s, which is 3.1×103 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.

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