“…Importantly, note that the weight change in the 1T1R synapse can be induced only via spike overlap, hence only for delays in the range −10 ms < Δt < 10 ms in this experiment [152]. Although the STDP characteristics achieved in the 1T1R RRAM synapse [151,152] display a squared shape due to binary operation of the RRAM cell instead of the exponentially decaying behavior observed in biological experiments, the plasticity of the 1T1R synapse was exploited in Although the STDP characteristics achieved in the 1T1R RRAM synapse [151,152] display a squared shape due to binary operation of the RRAM cell instead of the exponentially decaying behavior observed in biological experiments, the plasticity of the 1T1R synapse was exploited in many SNN implementations enabling neuromorphic tasks, such as unsupervised learning of space/spatiotemporal patterns [151,152,154,155], the extraction of auditory/visual patterns [156,157], pattern classification [158][159][160], and associative memory [161][162][163], in both simulation and hardware. Figure 16a shows the schematic representation of the RRAM-based SNN used in ref.…”