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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- Digital object identifier: 10.3389/fnins.2013.00186
- Identifier TypeInternational standard serial numberIdentifier Value1662-4548
- Identifier TypeInternational standard serial numberIdentifier Value1662-453X
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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