Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits
Hardware implementation of spiking neural networks holds promise for high energy efficient brain-inspired computing. Here, Prezioso et al. realize the detection of synchrony in a demo circuit composed of 20 metal-oxide memristor synapses connected to a leaky-integrate-and-fire silicon neuron.
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2018
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f5a2989a3d83440c9f152432cf481ea8 |
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| Sumario: | Hardware implementation of spiking neural networks holds promise for high energy efficient brain-inspired computing. Here, Prezioso et al. realize the detection of synchrony in a demo circuit composed of 20 metal-oxide memristor synapses connected to a leaky-integrate-and-fire silicon neuron. |
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