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| Hauptseite > Publikationsdatenbank > Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors > print |
| Hauptseite > Publikationsdatenbank > Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors > print |
| 001 | 860292 | ||
| 005 | 20210131030900.0 | ||
| 024 | 7 | _ | |a 10.1002/adfm.201704455 |2 doi |
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| 100 | 1 | _ | |a Yang, Rui |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors |
| 260 | _ | _ | |a Weinheim |c 2018 |b Wiley-VCH |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a The synaptic weight modification depends not only on interval of the pre‐/postspike pairs according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing of the preceding spike (triplet‐STDP). Triplet‐STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short‐term suppression effect of preceding spikes. Second‐order memristors with one state variable possessing short‐term dynamics work in a way similar to the biological system. In this work, the suppression triplet‐STDP learning rule is faithfully demonstrated by experiments and simulations using second‐order memristors. Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron is simulated using a circuit constructed with second‐order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet‐STDP rule, are realized. The realized weight‐dependent pair‐ and triplet‐STDP rules are clearly in line with findings in biology. The physically realized triplet‐STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second‐order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain‐inspired computation systems. |
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| 773 | _ | _ | |a 10.1002/adfm.201704455 |g Vol. 28, no. 5, p. 1704455 - |0 PERI:(DE-600)2039420-2 |n 5 |p 1704455 - |t Advanced functional materials |v 28 |y 2018 |x 1616-301X |
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