论文标题
用于在线无监督聚类的神经形态范式
A Neuromorphic Paradigm for Online Unsupervised Clustering
论文作者
论文摘要
提出了基于神经科学概念的计算范式，并证明可以在线无监督聚类。因为它是一种在线方法，所以它很容易适合流式实时应用程序，并且能够动态调整到宏观级输入更改。所有培训和推论的所有操作都是局部和有效的。该范式被实现为结合了五个关键要素的认知列：1）时间编码，2）推理的兴奋性神经元模型，3）赢家全部抑制作用，4）结合激发和抑制的柱体系结构，5）5）通过峰值定时DE-PENDENT DE-PENDENT DE-PENDENT DENEDENT PRASITITY（STDP）局部化训练。描述和讨论了这些元素，并给出了原型列。原型柱使用半合成基准进行模拟，并显示出具有经典K均值的性能特征。模拟揭示了列的内部操作和功能，重点是兴奋性神经元反应功能和STDP实现。
A computational paradigm based on neuroscientific concepts is proposed and shown to be capable of online unsupervised clustering. Because it is an online method, it is readily amenable to streaming realtime applications and is capable of dynamically adjusting to macro-level input changes. All operations, both training and inference, are localized and efficient. The paradigm is implemented as a cognitive column that incorporates five key elements: 1) temporal coding, 2) an excitatory neuron model for inference, 3) winner-take-all inhibition, 4) a column architecture that combines excitation and inhibition, 5) localized training via spike timing de-pendent plasticity (STDP). These elements are described and discussed, and a prototype column is given. The prototype column is simulated with a semi-synthetic benchmark and is shown to have performance characteristics on par with classic k-means. Simulations reveal the inner operation and capabilities of the column with emphasis on excitatory neuron response functions and STDP implementations.
