Inspired by experimental neuroscience results we developed a family of online algorithms that reduce dimensionality, cluster and discover features in streaming data. The novelty of our approach is in starting with similarity matching objective functions used offline in Multidimensional Scaling and Symmetric Nonnegative Matrix Factorization. We derived online distributed algorithms that can be implemented by biological neural networks resembling brain circuits. Such algorithms may also be used for Big Data applications.