Fast speed and a high accuracy implementation of
biological plausible neural networks are vital key objectives to
achieve new solutions to model, simulate and cure the brain diseases.
Efficient hardware implementation of spiking neural networks
is a significant approach in biological neural networks. This
paper presents a multiplierless noisy Izhikevich neuron (MNIN)
model, which is used for the digital implementation of biological
neural networks in large scale. Simulation results show that
the MNIN model reproduces the same operations of the original
noisy Izhikevich neuron. The proposed model has a low-cost hardware
implementation property compared with the original neuron
model. The field-programmable gate array realization results
demonstrated that the MNIN model follows the different spiking
patterns appropriately.