Polyaniline-Based Memristive Devices as Key Elements of Robust Reservoir Computing for Image Classification

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.

Automated summary

One remarkable feature of emerging neuromorphic systems is the ability to implement in-memory computing using memristors, which can achieve both memory and computation functions within a single element. However, a challenging perspective in the field is the identification and realization of systems that can imitate the sensitivity to temporal parameters of neural activity exhibited by biological neural systems. This study demonstrates polyaniline-based organic memristive devices with volatile resistive switching, complex temporal behaviors, and the ability to process 4-bit sequences of data with reliable separation of states. Through simulations, the study shows that these devices can be valuable elements in a reservoir computing system for classifying handwritten digits from the MNIST dataset, ensuring correct classification and tolerance to overlapping reservoir states.