The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. In this paper from Jiangsu University, researchers propose a fast spatial pool learning algorithm of HTM based on minicolumn’s nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding, to overcome these disadvantages.
In this doctoral dissertation from Nova Southeastern University, the author developed an HTM sequence classifier aimed at classifying streaming data, which contained concept drift, noise, and temporal dependencies. The HTM sequence classifier was fed both artificial and real-world data streams and evaluated using the prequential evaluation method. Furthermore, this research explored the suitability of the HTM sequence classifier for detecting stalling code within evasive malware. It highlights the potential of HTM technology for application in online classification problems and the detection of evasive malware.
Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on HTM, or hierarchical temporal memory, that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing.
This study presents a novel biometric approach to identify operators, given only streams of their control movements within a manual control task setting. In the present task subjects control a simulated, remotely operated robotic arm, attempting to dock onto a satellite in orbit. The proposed methodology utilizes the Hierarchical Temporal Memory (HTM) algorithm to distinguish operators by their unique control behaviors.
This paper seeks to answer the question of whether a neocortex-inspired model called MCN can combine with entorhinal cortex cells for space representation to exploit additional metric data like odometry. The authors argue such a combination of bio-inspired techniques could help someday to create a biologically plausible and more robust navigation system like in animals.
