KNOWM memristors in a bridge synapse delay-based reservoir computing system for detection of epileptic seizures

Przyczyna, Dawid; Hess, Grzegorz; Szaciłowski, Konrad

doi:10.1080/17445760.2022.2088751

Cited by 10 publications

(4 citation statements)

References 55 publications

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“…Furthermore, the changes occurring at low frequencies indicate, that slow physical phenomena (as diffusion) are critically responsible for the distortion of electric signals. This effect is similar to those observed in the case of solid-state memristor, however in the latter case the dependence is opposite [35]. It can be concluded that in the studied case at high frequencies only one, faster conductivity mode plays a significant role.…”

Section: Resultssupporting

confidence: 84%

Electrical frequency discrimination by fungi Pleurotus ostreatus

Przyczyna¹,

Szaciłowski²,

Chiolerio³

et al. 2022

Preprint

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We stimulate mycelian networks of oyster fungi Pleurotus ostreatus with low frequency sinusoidal electrical signals. We demonstrate that the fungal networks can discriminate between frequencies in a fuzzy or threshold based manner. Details about the mixing of frequencies by the mycelium networks are provided. The results advance the novel field of fungal electronics and pave ground for the design of living, fully recyclable, electron devices.

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Section: Resultssupporting

confidence: 84%

Electrical frequency discrimination by fungi Pleurotus ostreatus

Przyczyna¹,

Szaciłowski²,

Chiolerio³

et al. 2022

Preprint

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“…Feedback loop evolution enhances classification accuracy, and signal transformation alters complexity parameters, contributing to improved classification scores. 94) Among memristor-based RC systems, a variety of possible solutions, based on metal oxides, have been proposed through the literature. This is mostly because nonstochiometric oxides (from TiO 2 , WO 2 , HfO 2 ) have long been known to demonstrate memristive properties.…”

Section: -8mentioning

confidence: 99%

“…19). [121][122][123] This approach has already found a couple of physical implementations 94,124,125) and is pretty common in photonic systems. 99,[126][127][128][129][130][131][132][133] It is applicable to chemical sensing as well, even in very simplistic cases, as minute changes in the impedance of the layer at the electrode are translated into differences in signal evolution.…”

Section: -12mentioning

confidence: 99%

An organized view of reservoir computing: a perspective on theory and technology development

Abdi,

Mazur,

Szaciłowski

2024

Jpn. J. Appl. Phys.

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Reservoir computing is an unconventional computing paradigm that uses system complexity and dynamics as computational medium. Currently it is the leading computational paradigm in the fields of unconventional in materia computing. This review briefly outlines the theory behind the term ‘reservoir computing’, presents the basis for evaluation of reservoirs and presents a cultural reference of reservoir computing in haiku. The summary highlights recent advances in physical reservoir computing and points out the importance of the drive, usually neglected in physical implementations of reservoir computing. However, drive signals may further simplify the training of reservoirs’ readout layer training, thus contributing to improved performance of reservoir computer performance.

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“…CBRAM has also attracted attention in recent years as a device for neuromorphic system [5,6] and for reservoir computing system [7][8][9][10]. CBRAM's rich dynamics and nonlinearity appear to make it a viable candidate for new neuromorphic or reservoir computing systems that are currently being developed to replace traditional computing architectures to cope with increased demands for data processing [11][12][13][14]. CBRAM also offers efficient radio frequency switching solutions.…”

Section: Introductionmentioning

confidence: 99%

RF-wave induced CBRAM characteristic modulation specific to sheet-like conductive filaments

Yin,

Tsukamoto,

Hayashi

et al. 2023

Mater. Res. Express

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The shape of conductive filaments in CBRAM is important for resistance switching and conductance modulation, especially in applications like neuromorphic and reservoir computing that use conductance as weight. We report on RF-induced modulation of CBRAM using Ge2Sb3.4Te6.2 with sheet-like filaments and compared it to those with dendritic filaments. RF input below 100 MHz reduced SET and RESET voltages, similar to CBRAM with dendritic filaments, but showed significantly different resistance changes. Repeated RF on/off input gradually increased the resistance of low-resistance state, unlike the dendritic filament CBRAM, where the resistance decreased. The increased resistance suggests RF-induced denser sheet-like filaments. Furthermore, the resistance of the high-resistance state showed a peculiar RF-induced resistance change not observed in dendritic filaments. The resistance decreased during RF input and increased to nine times the initial value when RF was switched off. The results show that the conductance modulation by RF input strongly depends on the filament type.

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KNOWM memristors in a bridge synapse delay-based reservoir computing system for detection of epileptic seizures

Cited by 10 publications

References 55 publications

Electrical frequency discrimination by fungi Pleurotus ostreatus

Electrical frequency discrimination by fungi Pleurotus ostreatus

An organized view of reservoir computing: a perspective on theory and technology development

RF-wave induced CBRAM characteristic modulation specific to sheet-like conductive filaments

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