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    Memristors Empower Spiking Neurons With Stochasticity

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    Type
    Article
    Authors
    Al-Shedivat, Maruan cc
    Naous, Rawan cc
    Cauwenberghs, Gert
    Salama, Khaled N. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Electrical Engineering Program
    Sensors Lab
    Date
    2015-06
    Permanent link to this record
    http://hdl.handle.net/10754/575739
    
    Metadata
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    Abstract
    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Journal on Emerging and Selected Topics in Circuits and Systems
    DOI
    10.1109/JETCAS.2015.2435512
    ae974a485f413a2113503eed53cd6c53
    10.1109/JETCAS.2015.2435512
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer Science Program; Electrical Engineering Program; Electrical Engineering Program; Sensors Lab; Sensors Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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