Abstract
Spiking Neural Networks (SNNs) reduce the computational complexity compared to traditional artificial neural networks (ANN) by introducing the spike coding method and the nonlinear activated neuron model and transmitting only the binary spike events. However, these complex model simulations and behavioral analysis are a standard approach of parametric values verification prior to their physical implementation on the hardware. Recently some popular tools have been presented, but we believe that none of the tools allow users to interact with the model simulation in run-time. The run-time interaction with the simulation creates a full understanding of these complex SNNs model mechanisms which is a quite challenging process, especially for early-stage researchers and students. In this paper, we present the first version of our novel spiking neural network user-friendly software tool named RAVSim (Real-time Analysis and Visualization Simulator), which provides a runtime environment to analyze and simulate the SNNs model. It is an interactive and intuitive tool designed to help in knowing considerable parameters involved in the working of the neurons, their dependency on each other, determining the essential parametric values, and the communication between the neurons for replicating the way the human brain works. Moreover, the proposed SNNs model analysis and simulation algorithm used in RAVSim takes significantly less time in order to estimate and visualize the behavior of the parametric values during a runtime environment.
This research was supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia.
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All of the experiments have been performed using run-time simulations on RAVSim v1.0. The RAVSim (v1.0) is an open-source simulator and it is published on LabVIEW official website and available publicly at [12]. The user manual [32] and video demonstration of RAVSim can be accessed at,
– https://www.youtube.com/watch?v=Ozv0MXXj89Y
Supporting Information. The Supporting Information including the detailed result of each test is available at [31]. The data for each test results is enclosed in its respective “Experimental Results” section.
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Acknowledgements.
The authors would like to thank Christopher Ostrau from the Center for Cognitive Interaction Technology (CITEC) at Bielefeld University, for his contribution to the development of earlier versions of RAVSim.
This research was supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia.
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Sanaullah, Koravuna, S., Rückert, U., Jungeblut, T. (2022). SNNs Model Analyzing and Visualizing Experimentation Using RAVSim. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_4
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