Modeling of Izhikevich Spiking Neurons With High Matching Accuracy Using Comb-Exp Functions and Its Multiplier-Free Realization

In the realm of neuroprosthetics, the imperative for hardware-efficient, faster spiking neuron models, reproducing spiking patterns with better precision, has emerged as a pivotal necessity; catalyzing advancements towards more compact and resource-optimal solutions for seamless integration within neural prosthetic systems. In this brief, a new digital model for Izhikevich Spiking neuron is proposed. The model accurately replicates the spiking patterns and phase portraits corresponding to the neuronal dynamic behaviour with a very high matching between the original and proposed models, high speed and improved hardware efficiency. The results of synthesis on the field programmable gate array (FPGA) platform demonstrate that the proposed hardware gives significant improvements in resource consumption and maximum operating frequency when compared to the latest existing Izhikevich spiking neuron architectures. A digit classification system using the proposed SNN model is implemented to demonstrate its applicability. Polychronous neuronal groups, which are ideal for signal-processing tasks like language learning and object recognition, are also simulated using the proposed model, which is significant in neuroprosthetic applications.