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Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive

Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning.

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    Date Created
    • 2013-10-31
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  • Text
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    Identifier
    • Digital object identifier: 10.3389/fnins.2013.00186
    • Identifier Type
      International standard serial number
      Identifier Value
      1662-4548
    • Identifier Type
      International standard serial number
      Identifier Value
      1662-453X

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    Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H. P. (2013). Stochastic learning in oxide binary synaptic device for neuromorphic computing. Frontiers in Neuroscience, 7. doi:10.3389/fnins.2013.00186

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