On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

Athanasiou, Vasileios; Konkoli, Zoran

doi:10.1080/17445760.2017.1287264

Cited by 19 publications

(17 citation statements)

References 11 publications

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“…Owing to their plasticity, analog nature, and (in most cases) nonvolatility, memristors constitute an emerging trend in modern electronics [6], representing a promising technology with several applications including The associate editor coordinating the review of this manuscript and approving it for publication was Omid Kavehei. memory [7], adaptive and learning circuits [8], [9], sensing [10] and computing [11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Memristor Multi-Level Tuning Dependencies on the Applied Pulse Properties via a Low Cost Instrumentation Setup

2019

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Deeper understanding of memristive behavior is the only safe way towards maximum exploitation of the favorable properties and the analog nature of this new device technology in innovative applications. This can be achieved through experimental hands-on experience with real devices. However, lab experiments with memristors are a challenging step, especially for the uninitiated. In this direction, this paper presents some important considerations to carry out reliable measurements using an experimental setup composed of off-the-shelf components and an affordable data acquisition system. We specifically show how a transimpedance amplifier can be used to protect the memristor from damage via current compliance limiting, and allow full control over the voltage drop on its terminals. Using the proposed setup, a set of key experiments were carried out on commercial memristors from Knowm Inc., revealing fundamental dependencies of memristor state-tuning properties on the characteristics of the applied pulses and the initial conditions of the devices.

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Section: Introductionmentioning

confidence: 99%

Exploring Memristor Multi-Level Tuning Dependencies on the Applied Pulse Properties via a Low Cost Instrumentation Setup

2019

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“…We believe a potential practical implementation of the electronic properties of fungi would be in sensorial and computing circuits embedded into mycelium bound composites. For example, an approach of exploiting reservoir computing for sensing [16], where the information about the environment is encoded in the state of the reservoir memristive computing medium, can be employed to prototype sensing-memritive devices from living fungi. A very low frequency of fungal electronic oscillators does not preclude us from considering inclusion of the oscillators in fully living or hybrid analog circuits embedded into fungal architectures [6] and future specialised circuits and processors made from living fungi functionalised with nanoparticles, as have been illustrated in prototypes of hybrid electronic devices with slime mould [68,63,47,4,20].…”

Section: Applications Of Fungal Electronicsmentioning

confidence: 99%

Fungal electronics

Adamatzky¹,

Ayres²,

Beasley³

et al. 2021

Preprint

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“…RC paradigm can be regarded as an extension of artificial neural networks (ANN) encompassing in its framework various physical substrates and processes [ 36 , 37 , 38 ]. Its main strength is the so-called “reservoir of states” possessing rich configuration state space of internal dynamics and performing nonlinear transformation of input signals.…”

Section: Introductionmentioning

confidence: 99%

Towards Embedded Computation with Building Materials

et al. 2021

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We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for “reservoir of states” necessary for simple tuning of the readout layer. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. As part of dynamic analysis, several fractal dimensions and entropy parameters for the output signal were analyzed to explore the richness of the reservoir configuration space. In addition, to investigate the chaotic nature and self-affinity of the signal, Lyapunov exponents and Detrended Fluctuation Analysis exponents were calculated. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.

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On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

Cited by 19 publications

References 11 publications

Exploring Memristor Multi-Level Tuning Dependencies on the Applied Pulse Properties via a Low Cost Instrumentation Setup

Exploring Memristor Multi-Level Tuning Dependencies on the Applied Pulse Properties via a Low Cost Instrumentation Setup

Fungal electronics

Towards Embedded Computation with Building Materials

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