There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner, and requires no prior knowledge of the number of clusters in the input data. This is achieved by finding correlated outputs from functions that capture commonly occurring input patterns. The algorithm can be deemed more biologically plausible than model optimization through backpropagation, although practical applicability may require additional research. However, the method yields satisfactory results on several toy datasets on a noteworthy range of hyperparameters.

翻译：目前尚无令人信服的证据表明反向传播是一种生物学上合理的机制，因此需要进一步研究替代学习算法。本文提出了一种新颖的在线聚类算法，该算法能够以无监督方式从输入中生成任意形状的聚类，且无需预先知道输入数据中的聚类数量。这是通过从捕获常见输入模式的函数中发现相关输出来实现的。与通过反向传播进行模型优化相比，该算法可被认为更具生物学合理性，尽管其实用性可能需要进一步研究。然而，该方法在多个合成数据集及一系列值得关注的超参数范围内均取得了令人满意的结果。