Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

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  • Anna N. Matsukatova
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Nikita V. Prudnikov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Vsevolod A. Kulagin
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Silvia Battistoni
    Consiglio Nazionale delle Ricerche Institute of Materials for Electronics and Magnetism (CNR-IMEM) Parco Area delle Scienze 37A 43124 Parma Italy
  • Anton A. Minnekhanov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Andrey D. Trofimov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Aleksandr A. Nesmelov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Sergey A. Zavyalov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Yulia N. Malakhova
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Matteo Parmeggiani
    Department of Applied Science and Technology (DISAT) Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy
  • Alberto Ballesio
    Department of Applied Science and Technology (DISAT) Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy
  • Simone Luigi Marasso
    Consiglio Nazionale delle Ricerche Institute of Materials for Electronics and Magnetism (CNR-IMEM) Parco Area delle Scienze 37A 43124 Parma Italy
  • Sergey N. Chvalun
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Vyacheslav A. Demin
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Andrey V. Emelyanov
    National Research Centre Kurchatov Institute 123182 Moscow Russia
  • Victor Erokhin
    Consiglio Nazionale delle Ricerche Institute of Materials for Electronics and Magnetism (CNR-IMEM) Parco Area delle Scienze 37A 43124 Parma Italy

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<jats:sec><jats:label/><jats:p>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.</jats:p></jats:sec>

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