Before revision:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain. Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions.
After revision:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) . Layered hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions.