Simulation of biological neural networks is a computationally intensive task due to the number of neurons, various communication pathways, and non-linear terms in the differential equations of the neuron.
This study proposes an original modification to optimize performance and power consumption in systems, simulating or implementing spiking neural networks. First, the proposed modified models were simulated for validation. Furthermore, digital hardware was designed, and both the original and proposed models were implemented on a Field-Programmable Gate Array (FPGA).
Moreover, the impact of the proposed modification on performance metrics was studied. The implementation results confirmed that the proposed models are considerably faster and require less energy to generate a spike compared with unmodified neurons.