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在尖端神经网络中发现巧合和整合行为

Andreas Stoll¹, Andreas Maier¹, Patrick Krauss^1,2

¹Pattern Recognition Lab, University Erlangen-Nürnberg, Erlangen, Germany.

Cognitive neurodynamics

|August 6, 2024

概括

使用漏洞整合和发射 (LIF) 神经元的尖端神经网络 (SNN) 呈现出不同的操作模式. 这项研究量化了这些模式,揭示了依赖于衰变时间的力量-规律关系,这对SNN效率和生物可信性至关重要.

科学领域:

计算神经科学是一种神经科学.
人工智能的人工智能

背景情况:

尖端神经网络 (SNN) 越来越多地被研究其潜在的能源效率和动态适应性,模仿生物神经网络.
泄漏,整合和燃烧 (LIF) 神经元是SNN中常见的模型,具有双重操作模式:根据膜衰变时间检测和整合巧合.
这些LIF神经元运行模式对SNN性能的确切出现和影响尚不清楚.

研究的目的:

研究不同衰变时间对SNN内的LIF神经元运行模式的影响.
建议和验证用于描述这些操作模式的定量措施.
探索衰变时间和已识别的操作模式之间的关系.

主要方法:

训练有素的SNN使用基于代理梯度的方法,具有不同的LIF神经元衰变时间.
引入了两项新措施:贡献输入峰数和有效集成间隔,以量化神经元运行模式.
分析了这些措施与神经元衰变时间之间的相关性.

主要成果:

巧合检测模式的特点是输入峰值较少,整合间隔较短.
集成模式与众多输入峰值和延长的集成间隔有关.
在这两项测量结果之间发现了线性相关性,其中一个相关系数表现出对衰变时间的功率定律依赖.

结论:

确定的措施有效地区分了LIF神经元中的巧合检测和集成模式.
权力法关系表明LIF网络与衰变时间相关的内在属性.
这项研究为通过理解和控制神经元操作模式来优化SNNs以提高效率和生物现实性提供了基础.

关键词:

人工智能的人工智能是人工智能.巧合检测检测系统巧合检测系统计算建模计算建模泄漏-整合-和-火神经元的神经元.神经网络的神经网络的神经网络

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