A Proposal and Implementation of a Neural Network Based Hierarchical Temporal Memory to Realize Cognitive Functions

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A Proposal and Implementation of a Neural Network Based Hierarchical Temporal Memory to Realize Cognitive Functions


Hierarchical Temporal Memory (HTM) is a recent innovation in cognition science. Developed in 2005 by Numenta Inc., an artificial intelligence research firm in the US, HTMs attempt to capture the way the human brain learns and infers its environment. One of the most notable characteristics of this model is the consideration of the hierarchical organization of objects in the world. Data in the world is made up of elementary features that aggregate in successive layers to form perceivable objects. This data can be visual, auditory or from other abstract spaces such as stock markets and scientific studies. The amount of raw data that the brain is exposed to throughout its lifetime is beyond imagination. However the brain is known to use a very noble and systematic approach to handle the perception, storage, and inference of this data. Several studies in neuroscience and psychology indicate that the brain makes use of the hierarchies that features in the world exhibit in their organization to form objects. Hence, for instance, ‘corners’ and ‘lines’ can aggregate to form a ‘table’ object in the visual world. These elementary features, however, can use a different aggregation to form a ‘chair’ object. The same is true for data in other types of worlds such as audio. HTMs directly apply a similar handling of world data for their cognition. Furthermore, the structure of HTMs, made up of data processing nodes arranged in a hierarchical tree, mimic the physical arrangement of cortical layers in the brain. The various data analysis algorithms in the nodes of HTMs were, however, found by the researcher to be limiting in several aspects, one of which is handling of unforeseen (untrained) data. For this purpose neural network data structures were used to replace some of the operations in these nodes for their ability in approximating untrained data to nearest matches. This research work discusses the proposed model, implementation constraints, the operational characteristics, and performance enhancements observed in this modified model with a selected test application. A substantial improvement in cognitive functionality has been observed with the newly proposed model.



Hierarchical, Temporal