Hierarchical Temporal Memory (HTM) is a computational model of the neocortex that is capable of online learning to predict and detect anomalies from continuous data streams. To make HTM also available on power-constrained robot systems, we investigate the feasibility of implementing the model on SpiNNaker, a fully programmable energy-efficient neuromorphic many core system. Our contribution is twofold: First, we propose a mapping of the HTM model components to the SpiNNaker chip architecture. Second, a prototypic implementation of this mapping is successfully evaluated for different sets of model parameters.