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.

Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements.