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Research Publications

Outside Research

  1. Sparse Associative Memory
    Hoffmann, Heiko
    Sequence Learning
    Published in Neural Computation2019/04/12

    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.'

  2. Non-contractual churn prediction using Hierarchical Temporal Memory
    Bakkevig, Jone K.; Methi, Magnus
    Sequence Learning
    Master's theses in Information and Communication Technology2018/09/21

    In this Master's thesis from the University of Agder, the authors examine using Hierarchical Temporal Memory (HTM) to predict future purchase events of customers in a non-contractual setting. HTM was chosen specifically because of a desire to research algorithms that may be useful in churn analysis by utilizing temporal structure of data in prediction-based models. The authors also compare HTM results to other techniques.

  3. Neuromorphic Architecture for the Hierarchical Temporal Memory
    Abdullah M. Zyarah, Dhireesha Kudithipudi
    Hardware implementations of HTM
    Pre-print of journal submission 2018/07/31

    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.

  4. Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System
    Petruț A. Bogdan*, Andrew G. D. Rowley, Oliver Rhodes and Steve B. Furber
    Hardware implementations of HTM
    Published in Frontiers in Neuroscience2018/07/02

    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.

  5. Detecting performance anomalies in scientific workflows using hierarchical temporal memory
    Maria A.Rodriguez, Ramamohanarao Kotagiri, Rajkumar Buyya
    Anomaly Detection
    Published in Future Generation Computer Systems2018/05/18

    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.

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