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.
In this paper, we investigate and evaluate the use of hierarchical temporal memory (HTM) for short-term prediction of traffic flows over real-world Sydney Coordinated Adaptive Traffic System data on arterial roads in the Adelaide metropolitan area in South Australia. Results are compared with those from long-short-term memory (LSTM). Extended experimentation with LSTM network configurations in both batch learning and online learning modes provide results with superior predictive performance over previous usage of LSTM and other deep learning techniques for short-term traffic flow prediction. In addition, we argue that HTM has potential as an effective tool for short term traffic flow prediction with results on par with LSTM and improvements when traffic flow distributions change.
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.’
In this Master Thesis from KTH Royal Institute of Technology, the author developed a machine learning model with Numenta’s HTM technology with the goal to solve the task of detecting and classifying anomalies in system logs belonging to Ericsson’s component based architecture applications. The results are then compared to an existing classifier, called Linnaeus, which uses classical machine learning methods. The HTM model is able to show promising capabilities of classifying system log sequences with similar results compared with the Linnaeus model.