论文标题
特征相似性揭示的神经形态网络
Neuromorphic Networks as Revealed by Features Similarity
论文作者
论文摘要
神经元形态的研究不仅对其与神经元动力学的潜在关系非常重要，而且还与物种，器官和条件相比，对各种类型的细胞和比较进行了分类。在目前的工作中，我们通过使用巧合相似性的概念以及一种将数据集映射到网络中的方法来解决这个有趣的问题。发现相似性的相似性允许一些特定的有趣属性，这些属性允许在几种模式识别任务方面提高性能（选择性和灵敏度）。考虑了20种形态学特征的几种组合，并通过分别将字面模块化（以监督方式）最大化到所涉及的参数来获得相应的网络。获得了良好的分离组，该组提供了735个被视为神经元细胞之间主要相似性相互关系的丰富表示。还通过改变一个巧合参数之一获得了一系列网络配置，说明了细胞和组之间的渐进性合并。
The study of neuronal morphology is important not only for its potential relationship with neuronal dynamics, but also as a means to classify diverse types of cells and compare than among species, organs, and conditions. In the present work, we approach this interesting problem by using the concept of coincidence similarity, as well as a respectively derived method for mapping datasets into networks. The coincidence similarity has been found to allow some specific interesting properties which have allowed enhanced performance (selectivity and sensitivity) concerning several pattern recognition tasks. Several combinations of 20 morphological features were considered, and the respective networks were obtained by maximizing the literal modularity (in supervised manner) respectively to the involved parameters. Well-separated groups were obtained that provide a rich representation of the main similarity interrelationships between the 735 considered neuronal cells. A sequence of network configurations illustrating the progressive merging between cells and groups was also obtained by varying one of the coincidence parameters.
