Continuous online sequence learning with an unsupervised neural network model

Understand

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments.

• Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory is recently proposed as a theoretical framework for sequence learning in the cortex.
• In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data.
• We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule.