  The bottom two layers of a neuromorphic architecture are designed and shown
to be capable of online clustering and supervised classification. An active
spiking dendrite model is used, and a single dendritic segment performs
essentially the same function as a classic integrate-and-fire point neuron. A
single dendrite is then composed of multiple segments and is capable of online
clustering. Although this work focuses primarily on dendrite functionality, a
multi-point neuron can be formed by combining multiple dendrites. To
demonstrate its clustering capability, a dendrite is applied to spike sorting,
an important component of brain-computer interface applications. Supervised
online classification is implemented as a network composed of multiple
dendrites and a simple voting mechanism. The dendrites operate independently
and in parallel. The network learns in an online fashion and can adapt to
macro-level changes in the input stream. Achieving brain-like capabilities,
efficiencies, and adaptability will require a significantly different approach
than conventional deep networks that learn via compute-intensive back
propagation. The model described herein may serve as the foundation for such an
approach.
