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A wide range of evidence points toward the existence of a common algorithmunderlying the processing of information throughout the cerebral cortex.Several hypothesized features of this cortical algorithm are reviewed,including sparse distributed representation, Bayesian inference, hierarchicalorganization composed of alternating template matching and pooling layers,temporal slowness and predictive coding. Hierarchical Temporal Memory (HTM) isa family of learning algorithms and corresponding theories of cortical functionthat embodies these principles. HTM has previously been applied mainly toperceptual tasks typical of posterior cortex. In order to evaluate HTM as acandidate model of cortical function, it is necessary also to investigate itscompatibility with the requirements of frontal cortical function. To this end,a variety of models of frontal cortical function are reviewed and integrated,to arrive at the hypothesis that frontal functions including attention, workingmemory and action selection depend largely upon the same basic algorithms as doposterior functions, with the notable additions of a mechanism for the activemaintenance of representations and of multiple cortico-striato-thalamo-corticalloops that allow communication between regions of frontal cortex to be gated inan adaptive manner. Computational models of this system are reviewed. Finally,there is a discussion of how HTM can contribute to the understanding of frontalcortical function, and of what the requirements of frontal cortical functionmean for the future development of HTM.

Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function