Memristive Devices for Neuromorphic Applications: Comparative Analysis Victor Erokhin 1,2 Accepted: 2 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Neuromorphic systems must have at least five unavoidable features that are present in living beings. First, neuromorphic systems must perform memorizing and processing functions, using same elements. Second, it must allow data acquisition from sensors with preliminary processing and recording. Third, it must allow non-equilibrium processes, such as oscillator behavior at fixed values of input stimuli. Fourth, it must mimic some features of nervous systems. Fifth, it must permit possibility of coupling with living beings. In this paper, these features are considered with a special attention to how memristive devices can be implemented for reaching the goal. Comparison of characteristic properties of organic and inorganic memristive devices is discussed. Keywords Neuromorphic systems . Memristive devices . Memory and processing . Oscillators . Sensors 1 Introduction An explosive activity in the field of memristive device [1] applicability for neuromorphic systems during last decade is due to their capabilities of mimicking some properties of ele- ments of nervous systems in artificial circuits and networks. It is possible to distinguish such applications as memory, artifi- cial neural networks (ANN), sensors, oscillators, nervous sys- tem mimicking circuits, interfacing with living beings, which can result in the realization of prosthesis of some parts of the nervous system. These applications are strongly connected between each other. Same elements can be involved in performing different functions (for example: memorizing and processing), and therefore, it is possible to make only approximate division of tasks. Usually, neuromorphic appli- cations of memristive devices are connected to the possibili- ties of mimicking neuron and synapse properties in artificial circuits and systems, as it is indicated in reviews, dedicated to inorganic [2–6] and organic [7] memristive devices. In this short review, I will make some remarks regarding the areas of the applications of memristive devices for neuromorphic systems. Advantages and drawbacks of inor- ganic and organic systems for each application, mentioned above, will be also considered. The most of inorganic memristive devices are based on sandwich structures, where thin insulating layer is placed be- tween two metal electrodes. In most of the cases, the insulator layer is fabricated from metal oxide materials [8–12], depos- ited by different techniques. However, some other inorganic materials were also used for the memristive device fabrication, such as, for example, ferroelectrics [13, 14] and silicon nano- structures [15]. In most of the cases, the mechanism of the resistance switching in inorganic memristive devices is based on the formation of conductive filaments in dielectric layers with successive redox processes in them [16]. In the case of organic memristive devices, the most studied ones are based on polyaniline, which will be considered in detail in the successive sections. Other materials are P(VDF- TrFE) [17], fibroin [18], PEDOT/PSS [19], DNA [20], albu- min [21], etc. The application of memristive devices for computer mem- ory was the first one [22] and it is the most popular among all possibilities. The effective use of memristive devices as mem- ory elements in conventional computers requires such proper- ties as high density and non-volatility of the recording units. Arrays (mostly used crossbar configurations) of inorganic (mostly metal oxide based) memristive devices can corre- spond to these requirements [23–33]. A separate class of memory elements, using memristive devices, is based on spintronic principles [34]. * Victor Erokhin victor.erokhin@imem.cnr.it 1 IMEM-CNR, Institute of Materials for Electronics and Magnetism, Parco Area delle Scienze, 37A, 43124 Parma, Italy 2 Natinal Research Centre “Kurchatov Institute”, Akademika Kurchatova square 1, Moscow, Russian Federation 123182 BioNanoScience https://doi.org/10.1007/s12668-020-00795-1