Will X. Y. Li

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A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the(More)
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding(More)
This paper intends to clarify the process of the Perceptual Assimilation Model (PAM) predicting the patterns of human categorical speech perception in audio perception of second language (L2) speech signals. The original stage of categorizing acoustic stimuli in the L2 signal often involves assimilation from L1 categories. Whether L1 sounds will assimilate(More)
A new simulation scheme of the Digital Spiking Silicon Neuron (DSSN) model is proposed. This scheme is based on the reconfigurable dataflow computing paradigm and targets the Maxeler MaxWorkstation. Compared to the previous implementation of the DSSN network, the new scheme has the virtues of better flexibility and better programmability. More importantly,(More)
One important step towards the cognitive neural prosthesis design is to achieve real-time prediction of neuronal firing pattern. An FPGA-based hardware computational platform is designed to guarantee this hard real-time signal processing requirement. The proposed platform can work in dual modes: generalized Laguerre-Volterra model parameters estimation and(More)
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(More)
Category formation of human perception is a vital part of cognitive ability. The disciplines of neuroscience and linguistics, however, seldom mention it in the marrying of the two. The present study reviews the neurological view of language acquisition as normalization of incoming speech signal, and attempts to suggest how speech sound category formation(More)
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(More)
In this paper, we propose an FPGA-based hardware architecture for conducting real-time prediction of neural activity using a second-order generalized Laguerre-Volterra model (GLVM). This architecture serves as a rapid prototype of the prediction module of the future cognitive neural prosthetic device. We validate the functionality of the hardware model by(More)
Recent studies have verified the efficiency of stochastic state point process filter (SSPPF) in coefficients tracking in the modeling of the mammalian nervous system. In this study, a hardware architecture of SSPPF is both designed and implemented on a field-programmable gate array (FPGA). It provides a timeefficient method to investigate the nonlinear(More)