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Despite the recent success of artificial neural networks, more biologicallyplausible learning methods may be needed to resolve the weaknesses ofbackpropagation trained models such as catastrophic forgetting and adversarialattacks. Although these weaknesses are not specifically addressed, a novellocal learning rule is presented that performs online clustering with an upperlimit on the number of clusters to be found rather than a fixed cluster count.Instead of using orthogonal weight or output activation constraints, activationsparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuringthat multiple neuron centers cannot occupy the same location in the inputdomain. An update method is also presented for adjusting the widths of theGaussian neurons in cases where the data samples can be represented by meansand variances. The algorithms were applied on the MNIST and CIFAR-10 datasetsto create filters capturing the input patterns of pixel patches of varioussizes. The experimental results demonstrate stability in the learned parametersacross a large number of training samples.

Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters