Time Step Impact on Performance and Accuracy of Izhikevich Neuron: Software Simulation and Hardware Implementation

摘要

Spiking neurons, the models that mimic the biological cells in the brain, are described using ordinary differential equations. A common method to numerically solve these equations is Euler's method. An important factor that has a significant impact on the performance and cost of the hardware implementation or software simulation of spiking neural networks and yet its importance has been neglected in the published literature, is the time step in Euler's method. In this paper, first the Izhikevich neuron's accuracy as a function of the time step was measured. It was uncovered that the threshold time step that Izhikevich neuron becomes unstable is an exponential function of the input current. Software simulation performance, including total computational time and memory usage were compared for different time steps. Afterwards, the model was synthesized and implemented on the Filed Programmable Gate Array (FPGA). Hardware performance metrics such as speed, area and power consumption were measured for each time step. Results indicated that time step has a negative linear effect on the performance. It was concluded that by determining maximum input current to the neuron, larger time steps comparable to those used in the previous works could be employed.