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

Emelyanov, A. V.; Lapkin, Dmitry; Демин, В. А.; Erokhin, Victor; Battistoni, Silvia; Baldi, G.; Dimonte, Alice; Коровин, А. Н.; Iannotta, Salvatore; Кашкаров, П. К.; Kovalchuk, M. V.

doi:10.1063/1.4966257

Cited by 97 publications

(68 citation statements)

References 26 publications

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“…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]. Instead, the application of the spectroscopic technique allows monitoring of the conductivity state of all elements of the network in a real time for rather large area (up to half a meter), what will simplify, for example, the application of back propagation learning algorithms.…”

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

“…The actual output value of the neural network is denoted by y j and the ideal tag value is denoted by t j , and we can use the mean square error as an error function Figure 13. Logic scheme of the implemented neural network with two inputs, two hidden and one output neurons [24].…”

Section: Mnn Algorithmmentioning

confidence: 99%

“…The artificial neuron body (soma) was implemented in the circuit by an op-amp based differential adder and a voltage divider with a MOSFET controlled by the output of the summator [24]. This element executed the basic neuron functions in terms of information processing-summation and threshold.…”

Section: Mnn Circuitsmentioning

confidence: 99%

Memristor Neural Network Design

Huang¹,

Zhang²,

Li³

et al. 2018

Memristor and Memristive Neural Networks

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Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system-based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy-efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. In this chapter, the design of different neural network architectures based on memristor is introduced, including spiking neural networks, multilayer neural networks, convolution neural networks, and recurrent neural networks. And the brief introduction, the architecture, the computing circuits, and the training algorithm of each kind of neural networks are presented by instances. The potential applications and the prospects of memristor-based neural network system are discussed.

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“…10.46095.17099 Мемристор -элемент электрической цепи, способный изменять свое сопротивление в зависимости от величины электрического поля и протекшего заряда [1]. Интерес к изучению мемристоров обусловлен перспективами их применения для создания энергонезависимой памяти с произвольным доступом (RRAM), обладающей низким энергопотреб-лением при записи информации (∼ 10 −16 J/bit) [2,3], и нейроморфных систем [4][5][6]. При этом возможность задавать различные промежуточ-ные состояния мемристора является необходимым условием для его ис-пользования в качестве аналога синапса в нейроморфных приложениях (распознавание образов и естественного языка, способности к обуче-нию, обобщениям и пр.).…”

Section: поступило в редакцию 30 октября 2017 гunclassified

Прецизионный Алгоритм Переключения Мемристора В Состояние С Заданным Сопротивлением

Никируй¹,

Емельянов²,

Демин³

et al. 2018

Письма В Журнал Технической Физики

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An algorithm of memristor switching with high precision to a state with preset resistance has been developed based on the application of voltage pulses with smoothly increasing amplitude and the duration varying randomly within preset limits. It is shown that the proposed algorithm can be implemented in memristor structures based on (Co_40Fe_40B_20)_ x (LiNbO_3)_100– x nanocomposites with x ≈ 10 at. %. Optimum parameters are selected for the algorithm operation with a minimum number of iterations that allows the accuracy of resistance setting to be no worse than 0.5%. The obtained results can be used in the creation of neuromorphic systems.

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

Cited by 97 publications

References 26 publications

Computers from Plants We Never Made: Speculations

Computers from Plants We Never Made: Speculations

Memristor Neural Network Design

Прецизионный Алгоритм Переключения Мемристора В Состояние С Заданным Сопротивлением

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