A penalized maximum likelihood approach to the adaptive learning of the spatial pooler permanence

Hierarchical Temporal Memory is a machine learning algorithm for spatio-temporal information processing. One of the key functional units in this algorithm is the spatial pooler, which has been demonstrated to be efficient in classification, dimensionality reduction and for preprocessing non-spatial inputs. Formalization of the spatial pooler is proposed in recent literature. In this work, we present a principled theoretical formulation of the spatial poolers underlying learning scheme. Constraints from the active connected and emmeshed columns in a spatial pooler are included in the analysis. It has been observed that locally adaptive learning enhanced the performance of the spatial pooler as a feature selector.