- AutorIn
- Nan Du
- Mahdi Kiani
- Christian G. Mayr
- Tiangui You
- Danilo Bürger
- Ilona Skorupa
- Oliver G. Schmidt
- Heidemarie Schmidt
- Titel
- Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25ms to 125µs
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa-171458
- Quellenangabe
- Front. Neurosci. 9:227. doi: 10.3389/fnins.2015.00227
- Abstract (EN)
- Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one postsynaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25ms to 125μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.
- Andere Ausgabe
- DOI: 10.3389/fnins.2015.00227
- Link zur Originalpublikation in der Zeitschrift Frontiers in Neuroscience, Section Neuromorphic Engineering
Link: http://journal.frontiersin.org/article/10.3389/fnins.2015.00227/abstract - Freie Schlagwörter (DE)
- BFO basierter Memristor, Künstliche Synapsen, Technische Universität Chemnitz, Publikationsfonds
- Freie Schlagwörter (EN)
- BFO based memristor, artificial synapse, Technische Universität Chemnitz, Publication funds
- Klassifikation (DDC)
- 600
- Normschlagwörter (GND)
- Synapse, Memristor
- Herausgeber (Institution)
- Technische Universität Chemnitz
- Verlag
- Frontiers Research Foundation
- URN Qucosa
- urn:nbn:de:bsz:ch1-qucosa-171458
- Veröffentlichungsdatum Qucosa
- 18.06.2015
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch