The memristor-based associative learning network with retention loss

The memristor is an emerging device in nanotech-nology, which provides nanoscale size, low power consumption and high density. It has been widely used in neural networks where memristors are utilised as synaptic weights (connection sites) between neurons. In particular, current research focuses on the spiking-time dependant plasticity (STDP) which is an important synaptic modification rule based on the timings of the pre- and postsynaptic spikes. It has been revealed that the memristor is intrinsically similar with a synapse and capable to mimic the long-term depression (LTD) and long-term potentiation (LTP) behaviours under the STDP rule. Further studies show that the associative learning can be mimicked by memristive neural networks through proper learning rules. However, such studies focus on demonstrating the process of building the association, which lacks the retention loss process of forgetting the association. In this paper, a rate-based term is proposed to improve the previous model, and therefore the retention loss process can be implemented in the given network. The results demonstrate that the network with the improved model can successfully reproduce the retention loss process meanwhile retaining the process of building the association.