In the HTM model (presumably the Numenta algorithm you're referring to), synaptic weights are updated with every new data point in discrete time steps (as opposed to continuous). In that sense, HTM is an online learning model. There was an experimental implementation of Temporal Memory (one component in HTM) that batched up some of those operations into phases, but that still happened in a single time step and that implementation has since been phased out (pardon the pun).
In the HTM model (presumably the Numenta algorithm you're referring to), synaptic weights are updated with every new data point in discrete time steps (as opposed to continuous). In that sense, HTM is an online learning model. There was an experimental implementation of Temporal Memory (one component in HTM) that batched up some of those operations into phases, but that still happened in a single time step and that implementation has since been phased out (pardon the pun).
For some additional literature on the topic, see: - "Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex", http://journal.frontiersin.org/article/10.3389/fncir.2016.00... - "Continuous Online Sequence Learning with an Unsupervised Neural Network Model", http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00893... - "The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding", http://www.biorxiv.org/content/early/2016/11/02/085035.abstr...