High-Performance and Scalable System Architecture for the Real-Time Estimation of Generalized Laguerre-Volterra MIMO Model From Neural Population Spiking Activity ABSTRACT:A hardware-based computational platform is developed to model the generalized Laguerre–Volterra (GLV) multiple-input multiple-output (MIMO) system which is essential in identification of the time-varying neural dynamics underlying spike activities. Time cost for model parameters estimation is greatly reduced by a significant enhancement of 3.1$,times 10^{3}~{rm x}$ in data throughput of the Xilinx XC6VSX475T field programmable gate array (FPGA)-based system compared to a C model running on an Intel i7–860 Quad Core processor. The processing core 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. The hardware platform is scalable with the utilization of different number of processing units within each stage. It is also easily extendable into a multi-FPGA structure to further enhance the computational capability. A hardware IP library is proposed for versatile neural models and applications. The implementation of the self-reconfiguring platform and its applications to future research of neural dynamics are explored. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Highly Accurate Dual-Band Cellular Field Potential Acquisition For Brain–Machine Interface Optimizing the Performances of a P300-Based Brain–Computer Interface in Ambulatory Conditions