Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

AbstractArtificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

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New Trends in Digital Data Storage for the Internet of Things

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.359363 ◽

2018 ◽

Vol 6 (3) ◽

pp. 359-363

Author(s):

A. Saxena ◽

◽

S. Sharma ◽

S. Dangi ◽

A. Sharma ◽

...

Keyword(s):

Internet Of Things ◽

Data Storage ◽

Digital Data ◽

The Internet ◽

Digital Data Storage ◽

The Internet Of Things

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Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers

Biomimetics ◽

10.3390/biomimetics6020032 ◽

2021 ◽

Vol 6 (2) ◽

pp. 32

Author(s):

Tomasz Blachowicz ◽

Jacek Grzybowski ◽

Pawel Steblinski ◽

Andrea Ehrmann

Keyword(s):

Data Processing ◽

Data Storage ◽

Domain Walls ◽

Data Transfer ◽

Synaptic Activity ◽

Neuromorphic Computing ◽

Rotating Magnetic Fields ◽

Micromagnetic Simulations ◽

Energy Consuming ◽

Stochastic Data

Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.

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Analog and digital data storage in a phase-coded holographic memory

10.1117/12.304980 ◽

1998 ◽

Cited By ~ 3

Author(s):

Kai-Oliver Mueller ◽

Cornelia Denz ◽

Torsten Rauch ◽

Thorsten Heimann ◽

J. Trumpfheller ◽

...

Keyword(s):

Data Storage ◽

Digital Data ◽

Holographic Memory ◽

Digital Data Storage

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Instance Selection

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch161 ◽

2011 ◽

pp. 1041-1045

Author(s):

Huan Liu

Keyword(s):

Data Mining ◽

Data Storage ◽

Data Reduction ◽

Effective Means ◽

Digital Data ◽

Data Selection ◽

Instance Selection ◽

Redundant Data ◽

Performance Deterioration ◽

Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

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