FPGA architecture of generalized Laguerre-Volterra MIMO model for neural population activities

We present a full-parallelized and pipelined architecture for a generalized Laguerre-Volterra MIMO system to identify the time-varying neural dynamics underlying spike activities. The proposed architecture consists of a first stage containing a vector convolution and MAC (Multiply and Accumulation) component; a second stage containing a prethreshold potential updating unit with an error approximation function component; and a third stage consisting of a gradient calculation unit. A flexible and efficient architecture that can accommodate different design speed requirements is generated. The design runs on a Xilinx Virtex-6 FPGA and the processing core produces data samples at a speed of 1.33 × 106 data frames/sec, which is 3.1 × 103 times faster than the corresponding C model running on an Intel i7-860 Quad Core Processor. © 2011 IEEE.