Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection

Abstract

Our presented idea is to integrate artificial neural network (probably of BICA type) with a real biological network (ideally in the future with the human brain) in order to extend or enhance cognitive- and sensory- capabilities (e.g. by associating existing and artificial sensory inputs).We propose to design such neuro-module using Hierarchical Temporal Memory (HTM) which is a biologically-inspired model of the mammalian neocortex. A complex task of contextual anomaly detection was chosen as our case-study, where we evaluate capabilities of a HTM module on a specifically designed synthetic dataset and propose improvements to the anomaly model.HTM is framed within other common AI/ML approaches and we conclude that HTM is a plausible and useful model for designing a direct brain-extension module and draft a design of a neuromorphic interface for processing asynchronous inputs.Outcome of this study is the practical evaluation of HTM's capabilities on the designed synthetic anomaly dataset, a review of problems of the HTM theory and the current implemen- tation, extended with suggested interesting research direction for the future.