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Outside Research
Tue, Jul 31, 2018
Neuromorphic Architecture for the Hierarchical Temporal Memory
The authors present a full-scale Hierarchical Temporal Memory (HTM) architecture for spatial pooler and temporal memory and verify it for two data sets: MNIST and the European number plate font (EUNF), with and without noise. These results suggest that the proposed architecture, using a novel form of synthetic synapses, can serve as a digital core to build HTM in hardware.
Abdullah M. Zyarah and Dhireesha Kudithipudi
Mon, Jul 02, 2018
Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System
Structural plasticity is required by HTM models, but not addressed in many hardware architectures. This paper presents a model of topographic map formation that is implemented on the SpiNNaker neuromorphic platform, running in real time using point neurons, and making use of both synaptic rewiring and spike-timing dependent plasticity (STDP). The authors use our papers as one of the motivations for working on this architecture.
Petruț A. Bogdan*, Andrew G. D. Rowley, Oliver Rhodes and Steve B. Furber
Fri, Jun 15, 2018
Hierarchical Temporal Memory Software Agent: In the light of general artificial intelligence criteria
This project created a combination of Numenta’s HTM algorithms and Temporal Difference learning to solve simple tasks defined in a browser environment. The analysis and evaluation of the results show that the agent is capable of learning simple tasks and there is potential for generalization inherent to sparse representations.
Jakob Heyder
Fri, May 18, 2018
Detecting performance anomalies in scientific workflows using hierarchical temporal memory
This paper proposes an anomaly detection framework for scientific workflows that is based on our Hierarchical Temporal Memory (HTM) technology. The paper also cites the Numenta Anomaly Benchmark (NAB) scoring code and methodology; the researchers use NAB to evaluate performance of algorithms.
Maria A.Rodriguez, Ramamohanarao Kotagiri and Rajkumar Buyya
Tue, Jan 30, 2018
A Distributed Anomaly Detection System for In-Vehicle Network Using HTM Anomaly Detection papers
The authors proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) that predicts the flow of data in real time to increase security for a vehicular controller area network bus. The HTM-based anomaly detection system achieved better results than systems based on recurrent neural networks and hidden Markov model detection.
Chundong Wang, Zhentang Zhao, Liangyi Gong, Likun Zhu, Zheli Liu and Xiaochun Cheng
Tue, Oct 03, 2017
Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex
The experimental paper explores theories of predictive coding by presenting mice with repeated sequences of images where novel images are occasionally substituted. Many of the findings have strong relationships to the characteristics of HTM Sequence Memory. The discussion section of this paper explores this relationship and includes a paragraph on one of our papers, “Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.”
Jan Homann, Sue Ann Koay, Alistair M. Glidden, David W. Tank and Michael J. Berry II