  Sequence learning, prediction and replay have been proposed to constitute the
universal computations performed by the neocortex. The Hierarchical Temporal
Memory (HTM) algorithm realizes these forms of computation. It learns sequences
in an unsupervised and continuous manner using local learning rules, permits a
context specific prediction of future sequence elements, and generates mismatch
signals in case the predictions are not met. While the HTM algorithm accounts
for a number of biological features such as topographic receptive fields,
nonlinear dendritic processing, and sparse connectivity, it is based on
abstract discrete-time neuron and synapse dynamics, as well as on plasticity
mechanisms that can only partly be related to known biological mechanisms.
Here, we devise a continuous-time implementation of the temporal-memory (TM)
component of the HTM algorithm, which is based on a recurrent network of
spiking neurons with biophysically interpretable variables and parameters. The
model learns high-order sequences by means of a structural Hebbian synaptic
plasticity mechanism supplemented with a rate-based homeostatic control. In
combination with nonlinear dendritic input integration and local inhibitory
feedback, this type of plasticity leads to the dynamic self-organization of
narrow sequence-specific subnetworks. These subnetworks provide the substrate
for a faithful propagation of sparse, synchronous activity, and, thereby, for a
robust, context specific prediction of future sequence elements as well as for
the autonomous replay of previously learned sequences. By strengthening the
link to biology, our implementation facilitates the evaluation of the TM
hypothesis based on electrophysiological and behavioral data.
