The paper discusses the development of a new model for biological neural networks called N-LUT-ADEX, which uses a sampling method to convert continuous signals into discrete data.
This model achieves high accuracy in replicating the spiking patterns of the original Adaptive Exponential integrate-and-fire neuron model.
Implemented on a Virtex-II FPGA board, N-LUT-ADEX demonstrates low-cost and high-speed capabilities, making it a promising option for neuromorphic engineering applications.