基于脉冲神经网络的图像分类的运行时模拟器RAVSim评价。

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sanaullah, Shamini Koravuna, Ulrich Rückert, Thorsten Jungeblut
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引用次数: 1

摘要

脉冲神经网络(snn)通过构建神经元和突触来模拟人脑的电信号传输,从而帮助实现类似大脑的效率和功能。然而,最优SNN实现需要参数值的精确平衡。为了设计这种无处不在的神经网络,一个可视化、分析和解释尖峰内部行为的图形工具是至关重要的。尽管有一些流行的SNN模拟器可用,但这些工具不允许用户在模拟期间与神经网络进行交互。为此,我们推出了第一个运行时交互模拟器,称为运行时分析和可视化模拟器(RAVSim),用于分析和动态可视化snn的行为,允许最终用户交互,观察输出浓度反应,并在模拟过程中直接进行更改。在本文中,我们向RAVSim展示了使用具有不同连接方案的LIF神经模型、使用snn的图像分类模型和数据集创建功能的运行时交互的当前实现。我们的主要目标是主要研究使用RGB图像的snn进行二值分类。我们使用LIF神经模型创建了一个前馈网络,用于图像分类算法,并使用RAVSim对其进行了评估。该算法对有掩码和没有掩码的人脸进行分类,在一个隐藏层使用1000个神经元,MSE为0.0758,在CPU上的执行时间为~ 10 min,准确率达到91.8%。实验结果表明,使用RAVSim不仅可以提高网络设计速度,还可以提高用户的学习能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim.

Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality by building neurons and synapses that mimic the human brain's transmission of electrical signals. However, optimal SNN implementation requires a precise balance of parametric values. To design such ubiquitous neural networks, a graphical tool for visualizing, analyzing, and explaining the internal behavior of spikes is crucial. Although some popular SNN simulators are available, these tools do not allow users to interact with the neural network during simulation. To this end, we have introduced the first runtime interactive simulator, called Runtime Analyzing and Visualization Simulator (RAVSim),a developed to analyze and dynamically visualize the behavior of SNNs, allowing end-users to interact, observe output concentration reactions, and make changes directly during the simulation. In this paper, we present RAVSim with the current implementation of runtime interaction using the LIF neural model with different connectivity schemes, an image classification model using SNNs, and a dataset creation feature. Our main objective is to primarily investigate binary classification using SNNs with RGB images. We created a feed-forward network using the LIF neural model for an image classification algorithm and evaluated it by using RAVSim. The algorithm classifies faces with and without masks, achieving an accuracy of 91.8% using 1000 neurons in a hidden layer, 0.0758 MSE, and an execution time of ∼10[Formula: see text]min on the CPU. The experimental results show that using RAVSim not only increases network design speed but also accelerates user learning capability.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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