In biological synapses, the connection strength (plasticity) between two neurons is controlled by the ionic flow through the synaptic cleft and it is widely believed that the adaptation of synaptic weights enables biological systems to learn and function. Similarly, the conductance of a memristor depends on the history of the total charge that has travelled through it. A key feature of neuronal learning is habituation, whereby repeated stimuli strengthens the synaptic plasticity whilst a lack of stimuli results in weakening. Learning in biological systems also involves spike-timing-dependent plasticity (STDP). In STDP, learning the synaptic efficacy governing potentiation and depression is determined by the temporal order of pre-synaptic and post-synaptic spikes.
Using our optical memristor device, we demonstrate optical control of synaptic potentiation and depression, optical switching between short and long-term memory and optical modulation of the synaptic efficacy via spike timing dependent plasticity. The work opens the route to dynamic patterning of memristor networks both spatially and temporally by light, thus allowing the development of new optically reconfigurable neural networks and adaptive electronic circuits.2,3
References
- Jaafar, A. H. et al. Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems. Nanoscale 9, 17091 (2017).
- Kossifos, K. M., Antoniades, M. A., Georgiou, J., Jaafar, A. H. & Kemp, N. T. An Optically-Programmable Absorbing Metasurface. IEEE Int. Symp. Circuits Syst. (2018). doi:10.1109/ISCAS.2018.8351874
- Georgiou, J., Kossifos, K. M., Antoniades, M. A., Jaafar, A. H. & Kemp, N. T. Chua Mem-Components for Adaptive RF Metamaterials. IEEE Int. Symp. Circuits Syst. (2018). doi:10.1109/ISCAS.2018.8351852

