  A computational paradigm based on neuroscientific concepts is proposed and
shown to be capable of online unsupervised clustering. Because it is an online
method, it is readily amenable to streaming realtime applications and is
capable of dynamically adjusting to macro-level input changes. All operations,
both training and inference, are localized and efficient. The paradigm is
implemented as a cognitive column that incorporates five key elements: 1)
temporal coding, 2) an excitatory neuron model for inference, 3)
winner-take-all inhibition, 4) a column architecture that combines excitation
and inhibition, 5) localized training via spike timing de-pendent plasticity
(STDP). These elements are described and discussed, and a prototype column is
given. The prototype column is simulated with a semi-synthetic benchmark and is
shown to have performance characteristics on par with classic k-means.
Simulations reveal the inner operation and capabilities of the column with
emphasis on excitatory neuron response functions and STDP implementations.
