  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.
