泄漏的整合和燃烧神经元是复合Poisson过程的转变点检测器
Shivaram Mani1, Paul Hurley2, André van Schaik3
1International Centre for Neuromorphic Systems, MARCS Institute, Western Sydney University, Sydney, Australia shivarammani@gmail.com.
Neural computation
|March 20, 2025
概括
尖端神经元,就像漏洞的整合和发射模型,充当在线变化点检测器. 这些神经元可以快速识别神经活动中的微妙变化,挑战神经元仅仅是噪音设备的观点.
科学领域:
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
- 统计信号处理 统计信号处理
背景情况:
- 动物的神经系统通过神经活动的突然变化来检测环境变化.
- 变化点检测 (CPD) 算法通常用于分析这些变化.
- 很少有研究探讨尖端神经元作为固有的在线CPD代理.
研究的目的:
- 为了证明一个泄漏的整合和发射 (LIF) 神经元实现了在线CPD算法.
- 分析LIF神经元在其参数空间中的CPD性能.
- 调查LIF神经元的神经网络是否可以检测到尖端速率的变化.
主要方法:
- 模拟一个泄漏的整合和发射 (LIF) 神经元作为复合波桑过程的CPD算法.
- 在参数变化中量化LIF神经元的CPD性能.
- 分析LIF神经元的前网络,以检测输入速率变化.
主要成果:
- 一个LIF神经元被证明可以实现在线CPD算法.
- 一个LIF神经元的前网络在20ms内检测到输入速率的5%变化,罕见的假阳性.
- 在CPD的背景下,LIF神经元的关键电生理特征被统计解释.
结论:
- 尖端神经元,特别是LIF神经元,可以作为复杂的在线统计变化点检测器.
- LIF神经元的神经网络表现出高效可靠的检测微妙的输入速率变化.
- 这表明神经元的重新评估不是作为杂的单位,而是作为最佳统计算法的实施者.
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