An FPGA-Based High-Performance Neural Ensemble Spiking Activity Simulator Utilizing Generalized Volterra Kernel and Complexity Analysis

摘要

Neural information is represented and transmitted among neuronal units by a series of all-or-none "neural codes". During the process of neural prosthesis design, generally, a large amount of "neural codes" need to be captured and analyzed, which brings about an important discipline, known as neuroinformatics. However, 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 kernel-based neural spiking activity simulator is introduced for prediction of the neural codes in mammalian hippocampal region. High-performance VLSI architecture is established for the simulator based on high-order Volterra kernels involving cross-terms. The effectiveness and efficiency of the simulator are proven in experimental settings. This simulator has the potential to serve as a core functional unit in future hippocampal cognitive neural prosthesis.