Complex Spiking Neural Network Evaluated by Injury Resistance Under Stochastic Attacks.

IF 2.8 3区 医学 Q3 NEUROSCIENCES Brain Sciences Pub Date : 2025-02-13 DOI:10.3390/brainsci15020186
Lei Guo, Chongming Li, Huan Liu, Yihua Song
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Abstract

Background: Brain-inspired models are commonly employed for artificial intelligence. However, the complex environment can hinder the performance of electronic equipment. Therefore, enhancing the injury resistance of brain-inspired models is a crucial issue. Human brains have self-adaptive abilities under injury, so drawing on the advantages of the human brain to construct a brain-inspired model is intended to enhance its injury resistance. But current brain-inspired models still lack bio-plausibility, meaning they do not sufficiently draw on real neural systems' structure or function.

Methods: To address this challenge, this paper proposes the complex spiking neural network (Com-SNN) as a brain-inspired model, in which the topology is inspired by the topological characteristics of biological functional brain networks, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time delay co-regulated by excitatory synapses and inhibitory synapses. To evaluate the injury resistance of the Com-SNN, two injury-resistance metrics are investigated and compared with SNNs with alternative topologies under the stochastic removal of neuron models to simulate the consequence of stochastic attacks. In addition, the injury-resistance mechanism of brain-inspired models remains unclear, and revealing the mechanism is crucial for understanding the development of SNNs with injury resistance. To address this challenge, this paper analyzes the synaptic plasticity dynamic regulation and dynamic topological characteristics of the Com-SNN under stochastic attacks.

Results: The experimental results indicate that the injury resistance of the Com-SNN is superior to that of other SNNs, demonstrating that our results can help improve the injury resistance of SNNs.

Conclusions: Our results imply that synaptic plasticity is an intrinsic element impacting injury resistance, and that network topology is another element that impacts injury resistance.

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随机攻击下复杂刺突神经网络损伤抗力评价。
背景:受大脑启发的模型通常用于人工智能。然而,复杂的环境会阻碍电子设备的性能。因此,增强脑启发模型的抗损伤性是一个至关重要的问题。人脑在损伤下具有自适应能力,因此借鉴人脑的优点构建脑启发模型是为了增强其抗损伤能力。但目前的大脑启发模型仍然缺乏生物合理性,这意味着它们没有充分利用真实神经系统的结构或功能。方法:针对这一挑战,本文提出了一种基于脑启发的复杂尖峰神经网络(Com-SNN)模型,该模型的拓扑结构灵感来源于生物功能脑网络的拓扑结构特征,节点为Izhikevich神经元模型,边缘为突触可塑性模型,其延时由兴奋性突触和抑制性突触共同调节。为了评估Com-SNN的抗损伤性,研究了两个抗损伤指标,并与随机移除神经元模型下具有替代拓扑的snn进行了比较,以模拟随机攻击的后果。此外,脑启发模型的损伤抵抗机制尚不清楚,揭示其机制对于理解snn的损伤抵抗发展至关重要。针对这一挑战,本文分析了随机攻击下Com-SNN的突触可塑性、动态调控和动态拓扑特征。结果:实验结果表明,Com-SNN的抗损伤性优于其他snn,说明我们的研究结果有助于提高snn的抗损伤性。结论:突触可塑性是影响损伤抵抗的内在因素,网络拓扑结构是影响损伤抵抗的另一个因素。
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来源期刊
Brain Sciences
Brain Sciences Neuroscience-General Neuroscience
CiteScore
4.80
自引率
9.10%
发文量
1472
审稿时长
18.71 days
期刊介绍: Brain Sciences (ISSN 2076-3425) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications in the areas of cognitive neuroscience, developmental neuroscience, molecular and cellular neuroscience, neural engineering, neuroimaging, neurolinguistics, neuropathy, systems neuroscience, and theoretical and computational neuroscience. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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