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A Model for R(t) Elements and R(t) -Based Spike-Timing-Dependent Plasticity With Basic Circuit Examples.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Oct2020, Vol. 31 Issue 10, p4206-4216. 11p. - Publication Year :
- 2020
-
Abstract
- Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses is, therefore, of significant interest. While one popular method involves pulse-shaping the spiking neuron output voltages, an alternative approach is outlined in this article. The proposed STDP implementation uses time-varying dynamic resistance [R(t)] elements to achieve local synaptic learning from spike-pair STDP, spike triplet STDP, and firing rates. The R(t) elements are connected to each neuron circuit, thereby maintaining synaptic density and leveraging voltage division as a means of altering synaptic weight (memristor voltage). Example R(t) elements with their corresponding behaviors are demonstrated through simulation. A three-input-two-output network using single-memristor synaptic connections and R(t) elements is also simulated. Network-level effects, such as nonspecific synaptic plasticity, are discussed. Finally, spatiotemporal pattern recognition (STPR) using R(t) elements is demonstrated in simulation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 31
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 146358973
- Full Text :
- https://doi.org/10.1109/TNNLS.2019.2952768