The features of the main models of spiking neurons are discussed in this review. We focus
on the dynamical behaviors of five paradigmatic spiking neuron models and present recent …

Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …

The structure of the human brain has evolved to achieve extraordinary computing power
through continuous refinement by natural selection. At present, the topology of brain-like …

Simple Summary The brain is an incredibly complex system possessing outstanding abilities
to perform difficult tasks through a vast number of densely interconnected neurons. Aimed at …

Reduced precision number formats have become increasingly popular in various fields of
computational science, as they offer the potential to enhance energy efficiency, reduce …

Automatic signal modulation recognition in AI-based wireless communication can be done
using combinatorial deep learning neural network techniques to improve resource shortage …

This examination presents an original Broadband Oscillation Concealment Procedure in
Power Systems utilizing a Genetic Algorithm (GA). The philosophy's suitability is deliberately …

Abstract Spiking Neural Networks (SNNs) are a type of neural network designed to replicate
biological neural networks more closely by using discrete spikes to transmit information …

This paper presents an FPGA-based framework for real-time crosswalk segmentation,
employing Spiking Neural Networks (SNNs) and comparing the Integrate-and-Fire (IF) and …

Correction: Alkabaa et al. An Investigation on Spiking Neural Networks Based on the Izhikevich
Neuronal Model: Spiking Processin Page 1 Citation: Alkabaa, AS; Taylan, O.; Yilmaz, MT; …