Hardware elementary perceptron based on polyaniline memristive devices

Демин, В. А.; Erokhin, Victor; Emelyanov, A. V.; Battistoni, Silvia; Baldi, G.; Iannotta, Salvatore; Кашкаров, П. К.; Kovalchuk, M. V.

doi:10.1016/j.orgel.2015.06.015

Cited by 90 publications

(54 citation statements)

References 20 publications

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“…This property will allow to register the conductivity map of the whole formed network, while usually we have the possibility to measure only between fixed points where electrodes are attached. If we are thinking about the system with learning properties [42], it can be not enough we must register the variation of the connections between all elements of the network. In the case of a double-layer perceptron, for example, it required a realization of rather complicated external electronic circuit, providing the temporal detachment of individual elements from the network for the registering of its conductivity [46].…”

Section: Functionalizing Plantsmentioning

confidence: 99%

Computers from Plants We Never Made: Speculations

Adamatzky¹,

Harding²,

Erokhin

et al. 2017

Emergence, Complexity and Computation

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Plants are highly intelligent organisms. They continuously make distributed processing of sensory information, concurrent decision making and parallel actuation. The plants are efficient green computers per se. Outside in nature, the plants are programmed and hardwired to perform a narrow range of tasks aimed to maximize the plants' ecological distribution, survival and reproduction. To 'persuade' plants to solve tasks outside their usual range of activities, we must either choose problem domains which homomorphic to the plants natural domains or modify biophysical properties of plants to make them organic electronic devices. We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, α-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.

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Section: Functionalizing Plantsmentioning

confidence: 99%

Computers from Plants We Never Made: Speculations

Adamatzky¹,

Harding²,

Erokhin

et al. 2017

Emergence, Complexity and Computation

View full text Add to dashboard Cite

show abstract

“…And artificial neural networks -elementary single-layer perceptrons -have already been demonstrated on the basis of solid-state memristive nanocrossbar arrays [16] or organic memristive devices [17].…”

Section: Introductionmentioning

confidence: 99%

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Mikhaylov

Gryaznov

Belov

et al. 2016

Phys. Status Solidi C

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The breakthrough in electronics and information technology is anticipated by the development of emerging memory and logic devices, artificial neural networks and brain-inspired systems on the basis of memristive nano-materials represented, in a particular case, by a simple 'metal-insulator-metal' (MIM) thin-film structure. The present article is focused on the comparative analysis of MIM devices based on oxides with dominating ionic (ZrOx, HfOx) and covalent (SiOx, GeOx) bonding of various composition and geometry deposited by magnetron sputtering. The studied memristive devices demonstrate reproducible change in their resistance (resistive switching - RS) originated from the formation and rupture of conductive pathways (filaments) in oxide films due to the electric-field-driven migration of oxygen vacancies and/or mobile oxygen ions. It is shown that, for both ionic and covalent oxides under study, the RS behaviour depends only weakly on the oxide film composition and thickness, device geometry (down to a device size of about 20x20 mu m(2)). The devices under study are found to be tolerant to ion irradiation that reproduces the effect of extreme fluences of high-energy protons and fast neutrons. This common behaviour of RS is explained by the localized nature of the redox processes in a nanoscale switching oxide volume. Adaptive (synaptic) change of resistive states of memristive devices is demonstrated under the action of single or repeated electrical pulses, as well as in a simple model of coupled (synchronized) neuron-like generators. It is concluded that the noise-induced phenomena cannot be neglected in the consideration of a memristive device as a nonlinear system. The dynamic response of a memristive device to periodic signals of complex waveform can be predicted and tailored from the viewpoint of stochastic resonance concept. (C) 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

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“…Dealing with the hardware realization of a simple ANN, based on memristive devices, few are proposed in literature. [16][17][18][19] The single-layer (or elementary) perceptron is the simplest kind of neural network which can implement basic learning and parallel processing. However, to the best of our knowledge, there is no successful attempt of multilayer perceptron hardware realization on memristive devices.…”

Section: Introductionmentioning

confidence: 99%

First steps towards the realization of a double layer perceptron based on organic memristive devices

et al. 2016

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Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

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Hardware elementary perceptron based on polyaniline memristive devices

Cited by 90 publications

References 20 publications

Computers from Plants We Never Made: Speculations

Computers from Plants We Never Made: Speculations

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

First steps towards the realization of a double layer perceptron based on organic memristive devices

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