  Despite the recent success of artificial neural networks, more biologically
plausible learning methods may be needed to resolve the weaknesses of
backpropagation trained models such as catastrophic forgetting and adversarial
attacks. Although these weaknesses are not specifically addressed, a novel
local learning rule is presented that performs online clustering with an upper
limit on the number of clusters to be found rather than a fixed cluster count.
Instead of using orthogonal weight or output activation constraints, activation
sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring
that multiple neuron centers cannot occupy the same location in the input
domain. An update method is also presented for adjusting the widths of the
Gaussian neurons in cases where the data samples can be represented by means
and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets
to create filters capturing the input patterns of pixel patches of various
sizes. The experimental results demonstrate stability in the learned parameters
across a large number of training samples.
