SNNs Model Analyzing and Visualizing Experimentation Using RAVSim

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications

“…These groundbreaking works have garnered significant attention in the field, as they lay the foundation for high-level intelligence, accuracy, robustness, and energy efficiency. Therefore, some representative studies exemplify the state-of-the-art efforts in brain-inspired artificial intelligence, offering valuable and inspiring new directions for achieving AGI with unprecedented capabilities (Stimberg et al, 2019 ; Sanaullah et al, 2022a , b ). By combining the principles of neuroscience with advanced machine learning techniques, such as SNNs, these novel approaches hold the potential to revolutionize the field and drive AGI toward realization.…”

“…Despite the potential benefits of SNNs, there are still several challenges associated with their implementation. One of the most significant challenges is choosing the most appropriate SNN model for a given task (Stimberg et al, 2019 ; Sanaullah et al, 2022a , b ).…”

“…SNNs are a type of neural network model that has gained considerable attention in recent years due to their ability to simulate the behavior of biological neurons in the brain (Ghosh-Dastidar and Adeli, 2009b;Sanaullah et al, 2023a). SNNs are characterized by their discrete time steps, where the neurons generate spikes when the input reaches a certain threshold (Tavanaei et al, 2019;Sanaullah et al, 2020).…”

“…As AGI research progresses, these works will continue to serve as critical reference points for developing more efficient, intelligent, and adaptive artificial intelligence systems with realworld applications in diverse domains (Graves et al, 2008;Sanaullah et al, 2023a). Therefore, some critical research works have significantly contributed to the development of AGI through brain-inspired paradigms, highlighting their key contributions and implications for the future of artificial intelligence.…”

Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures

“…Additionally, the area as a whole also benefits from the study of other algorithms, such as training advances in the whole field. With this motivation and the difficulties that currently exist in comprehending and utilizing the promising features of SNNs, we proposed a novel run-time multi-core architecture-based simulator called "RAVSim" (Runtime Analysis and Visualization Simulator) [7], a cutting-edge SNN simulator, developed using LabVIEW [8] and it is publicly available on their website as an official module [9]. RAVSim is a runtime virtual simulation environment tool that enables the user to interact with the model, observe its behavior of output concentration, and modify the set of parametric values at any time while the simulation is in execution.…”