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It is well known that information is represented and transmitted among neuronal units by a series of all-or-none “neural codes”. In neuroinformatics study, this coding process, also termed as “spiking activity”, is not straightforward for prediction. It is owing to the high nonlinearity and dynamic property involved in generation of the neuronal spikes. In this paper, a novel generalized Volterra...
A generalized mathematical model is proposed for behaviors prediction of biological causal systems with multiple inputs and multiple outputs (MIMO). The system properties are represented by a set of model parameters, which can be derived with random input stimuli probing it. The system calculates predicted outputs based on the estimated parameters and its novel inputs. An efficient hardware architecture...
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, computing with dataflow cores takes good...
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 utilizing the neuronal firing data of behaving...
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...
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