Associative STDP-like learning of neuromorphic circuits based on polyaniline memristive microdevices

Spiking neuromorphic networks (SNNs) are bio-inspired artificial systems capable of unsupervised learning and promising candidates to mimic biological neural systems in efficient solution of cognitive tasks. Most SNNs are based on local learning rules, such as bio-like spike-time-dependent plasticity (STDP). In this paper, we report a significantly improved timescale of STDP for polyaniline-based memristive microdevices. We have used this result to show the possibility of associative learning with an unsupervised STDP-like mechanism of a simple SNN. The dependence of the required number of learning cycles on the pulse length was found: the longer the training pulse, the smaller the number of epochs the system needs to learn the associative rule. But the total training time remained nearly constant regardless of the pulse length. This study will be helpful in designing more sophisticated bio-plausible neuromorphic systems based on organic memristors. (paper)