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- W4366146985 abstract "Inspired by the human brain, neuromorphic computation should be extremely efficient at very large scales due to inherent parallelism, scalability, and fault and failure tolerance. Spike- Timing-Dependent Plasticity (STDP) is one of the most biologically plausible synaptic learning behaviors. The proposed generic model of time-varying resistance, or R(t) elements in this work can produce STDP in electronic spiking neural networks with memristive synapses that is very similar to that observed in biology. Both pair-based and triplet-based STDP is verified with the proposed generic R(t) model." @default.
- W4366146985 created "2023-04-19" @default.
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- W4366146985 date "2023-03-01" @default.
- W4366146985 modified "2023-10-01" @default.
- W4366146985 title "R(t)-Based Spike-Timing-Dependent Plasticity in Memristive Neural Networks" @default.
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- W4366146985 doi "https://doi.org/10.1109/wmed58543.2023.10097441" @default.
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