  Hierarchical Temporal Memory (HTM) is a computational theory of machine
intelligence based on a detailed study of the neocortex. The Heidelberg
Neuromorphic Computing Platform, developed as part of the Human Brain Project
(HBP), is a mixed-signal (analog and digital) large-scale platform for modeling
networks of spiking neurons. In this paper we present the first effort in
porting HTM networks to this platform. We describe a framework for simulating
key HTM operations using spiking network models. We then describe specific
spatial pooling and temporal memory implementations, as well as simulations
demonstrating that the fundamental properties are maintained. We discuss issues
in implementing the full set of plasticity rules using Spike-Timing Dependent
Plasticity (STDP), and rough place and route calculations. Although further
work is required, our initial studies indicate that it should be possible to
run large-scale HTM networks (including plasticity rules) efficiently on the
Heidelberg platform. More generally the exercise of porting high level HTM
algorithms to biophysical neuron models promises to be a fruitful area of
investigation for future studies.
