Impact of (Co-Fe-B)x(LiNbO3)100-x Nanocomposite Memristors Characteristics Dispersion on Dopamine-Like Modulation of Synaptic Plasticity

The use of memristors as modulators of synaptic connections is a promising direction in the development of neuromorphic computing systems (NCS), including those that use reinforcement learning. To implement the latter, spike-timing-dependent plasticity (STDP), depending on the time of arrival of pulses with dopamine-like modulation, can be used. Using an example of a memristor array based on a nanocomposite (Co-Fe-B)(x)(LiNbO3)(100-x) the possibility of changing the conductivity of memristor devices according to the STDP rules with dopamine-like modulation is studied, and the variation in the characteristics of the array memristors from cycle to cycle (C2C) and from device to device (D2D) is assessed. It is established that the D2D variation, compared to the C2C variation, has a greater impact on the STDP window, which must be taken into account when modeling and creating neural networks capable of reinforcement learning to solve complex cognitive tasks.