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.
In this paper, which Numenta VP Subutai Ahmad co-authored, the authors argue that despite the substantial advances deep learning has created in image and speech recognition, language translation, and game playing, the AI community should focus less on methods using nonbiological ANNs and more on the computational principles of the brain to create human-level or general AI with a performance close to humans at most cognitive tasks of interest.
This paper presents a new model of an associative memory that overcomes the typical deficiencies of spurious memories and low efficiency. The authors refer to it as associative memory (SAM) because it is based on sparse projections from neural patterns to pattern-specific neurons. This paper was inspired by the paper, ‘Why neurons have thousands of synapses, a theory of sequence memory in neocortex.’