论文标题
生物学上合理的单层网络，用于非负独立组件分析
Biologically plausible single-layer networks for nonnegative independent component analysis
论文作者
论文摘要
神经科学中的一个重要问题是了解大脑如何从未知来源的混合物中提取相关信号，即执行盲源分离。为了模拟大脑如何执行此任务，我们寻求盲源分离算法的生物学上合理的单层神经网络实现。对于生物学上的合理性，我们要求网络满足以下神经元电路的以下三个基本属性：（i）网络在在线环境中运行； （ii）突触学习规则是本地的； （iii）神经元输出无负。最接近的是Pehlevan等人的作品。 [神经计算，29，2925--2954（2017）]，它考虑了非负独立组件分析（NICA），这是一种盲源分离的特殊情况，假定混合物是不相关的非负源的线性组合。它们通过生物学上合理的2层网络实现得出了一种算法。在这项工作中，我们通过得出NICA的2种算法来改善它们的结果，每个算法都具有生物学上合理的单层网络实现。第一个算法映射到由中间神经元介导的间接横向连接的网络上。第二算法将直接侧向连接和多室输出神经元的网络映射到网络上。
An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm. For biological plausibility, we require the network to satisfy the following three basic properties of neuronal circuits: (i) the network operates in the online setting; (ii) synaptic learning rules are local; (iii) neuronal outputs are nonnegative. Closest is the work by Pehlevan et al. [Neural Computation, 29, 2925--2954 (2017)], which considers Nonnegative Independent Component Analysis (NICA), a special case of blind source separation that assumes the mixture is a linear combination of uncorrelated, nonnegative sources. They derive an algorithm with a biologically plausible 2-layer network implementation. In this work, we improve upon their result by deriving 2 algorithms for NICA, each with a biologically plausible single-layer network implementation. The first algorithm maps onto a network with indirect lateral connections mediated by interneurons. The second algorithm maps onto a network with direct lateral connections and multi-compartmental output neurons.
