Effect of the memristor threshold current on memristor-based Min-Max circuits

Amer, Sherif; Madian, Ahmed H.; ElSayed, Hany M.; Emara, Ahmed S.

doi:10.1109/mocast.2016.7495104

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Then we calculate the maximum value, minimum value, mean value, sum of absolute value and sum of energy for the voltage waveforms to obtain the 20 biomarkers (five features for each of four waveforms). These computations are currently done by software in this work for simplicity but in principle they could also be implemented by memristor-based electronics 56,57 . Furthermore, we normalize the extracted biomarkers to the range of 0.1-0.3 V by software using a linear transformation (e.g., y = a × x + b, x: input; y: output; a, b: amplification factor and offset), and then use them as the input voltages for the memristor-based neural network.…”

Section: Methodsmentioning

confidence: 99%

Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces

et al. 2020

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Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.

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Section: Methodsmentioning

confidence: 99%

Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces

et al. 2020

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“…Amer et al [23] discussed on the implementation of Center Of Gravity (COG). The combination of memristor and resistor is used to make two operators, minimum and maximum [24]. The implementation of T1F membership functions on memristor-crossbar structures is given by Merrikh-Bayat et al [25].…”

Section: The Memristor Modelingmentioning

confidence: 99%