Extension of neuron machine neurocomputing architecture for spiking neural networks

摘要: The neuron machine (NM) is a synchronous neurocomputing architecture that can be used to design efficient large-scale neural network simulation systems. However the NM has limitation it cannot support complex computations such as those for spiking (SNN) models. In this paper, we review and propose an extension of models with synaptic neuronal functions, by providing generalized memory structure methods pipelined circuits functions. addition, discuss designing simulator uses proposed implemented on field-programmable gate array (FPGA) board. We show 200 MHz mid-range FPGA run orders magnitude faster than most existing board-level implementations. additional advantages simplicity, accuracy, extensibility more biologically detailed compared event-driven approaches.