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.

One of the remarkable features of the emerging neuromorphic systems is the ability of implementing in‐memory computing which is demonstrated using memristors to realize both memory and computation functionalities within a single element. However, biological neural systems exhibit many other outstanding computing capabilities, among which one is the sensitivity to temporal parameters of neural activity. The identification and the realization of systems able to imitate this ability is still a very challenging perspective. Herein, polyaniline‐based organic memristive devices endowed with volatile resistive switching, complex temporal behaviors and capable of processing 4‐bit sequences of data with reliable separation of states are demonstrated. Thanks to this ability, such devices can be key elements in a reservoir layer of a network to map high‐dimensional input signals to a lower‐dimensional feature space. Herein, it is demonstrated through simulations that this type of device could be a valuable element for the realization of a reservoir computing system for the classification of handwritten digits from MNIST dataset. The model suggests that the electrical properties of the polyaniline‐based organic memristive devices ensure the realization of a system able to correctly classify handwritten digits and to be tolerant to considerable overlapping of neighboring reservoir states.

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