scholarly journals Graphene-Based Artificial Synapses with Tunable Plasticity

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
He Wang ◽  
Nicoleta Cucu Laurenciu ◽  
Yande Jiang ◽  
Sorin Cotofana
Keyword(s):  
Synaptic Plasticity ◽  
Weight Change ◽  
Time Scale ◽  
Large Scale ◽  
Synaptic Weight ◽  
Low Voltage ◽  
Logic Gates ◽  
Full Potential ◽  
Back Gate ◽  
Input Spike

Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired computation’s full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity . Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm 2 ), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

COMPOSITE TRANSISTOR CELL USING DYNAMIC BODY BIAS FOR HIGH GAIN AND LOW-VOLTAGE APPLICATIONS

2014 ◽  
Vol 23 (08) ◽  
pp. 1450108 ◽  
Author(s):  
VANDANA NIRANJAN ◽  
ASHWANI KUMAR ◽  
SHAIL BALA JAIN
Keyword(s):  
Low Power ◽  
Large Scale ◽  
Low Voltage ◽  
Supply Voltage ◽  
High Gain ◽  
Signal Frequency ◽  
Output Impedance ◽  
Self Cascode ◽  
Intrinsic Gain ◽  
Body Bias

In this work, a new composite transistor cell using dynamic body bias technique is proposed. This cell is based on self cascode topology. The key attractive feature of the proposed cell is that body effect is utilized to realize asymmetric threshold voltage self cascode structure. The proposed cell has nearly four times higher output impedance than its conventional version. Dynamic body bias technique increases the intrinsic gain of the proposed cell by 11.17 dB. Analytical formulation for output impedance and intrinsic gain parameters of the proposed cell has been derived using small signal analysis. The proposed cell can operate at low power supply voltage of 1 V and consumes merely 43.1 nW. PSpice simulation results using 180 nm CMOS technology from Taiwan Semiconductor Manufacturing Company (TSMC) are included to prove the unique results. The proposed cell could constitute an efficient analog Very Large Scale Integration (VLSI) cell library in the design of high gain analog integrated circuits and is particularly interesting for biomedical and instrumentation applications requiring low-voltage low-power operation capability where the processing signal frequency is very low.

scholarly journals Carrier-capture-assisted optoelectronics based on van der Waals materials to imitate medicine-acting metaplasticity

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Qianfan Nie ◽  
Caifang Gao ◽  
Feng-Shou Yang ◽  
Ko-Chun Lee ◽  
Che-Yi Lin ◽  
...  
Keyword(s):  
Weight Change ◽  
Synaptic Weight ◽  
Van Der Waals ◽  
Low Frequency ◽  
Photonic Devices ◽  
Frequency Noise ◽  
Carrier Capture ◽  
Electronic Engineering ◽  
Neural Simulation ◽  
Van Der Waals Materials

AbstractRecently, researchers have focused on optoelectronics based on two-dimensional van der Waals materials to realize multifunctional memory and neuron applications. Layered indium selenide (InSe) semiconductors satisfy various requirements as photosensitive channel materials, and enable the realization of intriguing optoelectronic applications. Herein, we demonstrate InSe photonic devices with different trends of output currents rooted in the carrier capture/release events under various gate voltages. Furthermore, we reported an increasing/flattening/decreasing synaptic weight change index (∆Wn) via a modulated gate electric field, which we use to imitate medicine-acting metaplasticity with effective/stable/ineffective features analogous to the synaptic weight change in the nervous system of the human brain. Finally, we take advantage of the low-frequency noise (LFN) measurements and the energy-band explanation to verify the rationality of carrier capture-assisted optoelectronics applied to neural simulation at the device level. Utilizing optoelectronics to simulate essential biomedical neurobehaviors, we experimentally demonstrate the feasibility and meaningfulness of combining electronic engineering with biomedical neurology.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Structure Controlled Synthesis of Vertically Aligned Carbon Nanotubes Using Thermal Chemical Vapor Deposition Process

2010 ◽  
Vol 133 (3) ◽  
Author(s):  
Myung Gwan Hahm ◽  
Young-Kyun Kwon ◽  
Ahmed Busnaina ◽  
Yung Joon Jung
Keyword(s):  
Carbon Nanotubes ◽  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Large Scale ◽  
Chemical Vapor ◽  
Controlled Synthesis ◽  
Full Potential ◽  
Thermal Chemical Vapor Deposition ◽  
Vertically Aligned ◽  
Walled Carbon Nanotubes

Due to their unique one-dimensional nanostructure along with excellent mechanical, electrical, and optical properties, carbon nanotubes (CNTs) become a promising material for diverse nanotechnology applications. However, large-scale and structure controlled synthesis of CNTs still have many difficulties due to the lack of understanding of the fundamental growth mechanism of CNTs, as well as the difficulty of controlling atomic-scale physical and chemical reactions during the nanotube growth process. Especially, controlling the number of graphene wall, diameter, and chirality of CNTs are the most important issues that need to be solved to harness the full potential of CNTs. Here we report the large-scale selective synthesis of vertically aligned single walled carbon nanotubes (SWNTs) and double walled carbon nanotubes (DWNTs) by controlling the size of catalyst nanoparticles in the highly effective oxygen assisted thermal chemical vapor deposition (CVD) process. We also demonstrate a simple but powerful strategy for synthesizing ultrahigh density and diameter selected vertically aligned SWNTs through the precise control of carbon flow during a thermal CVD process.

Recent Progress in SOI Materials and Devices for VLSI Applications

1993 ◽  
Vol 316 ◽  
Author(s):  
H. H. Hosack
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Recent Progress ◽  
Low Voltage ◽  
Silicon On Insulator ◽  
Significant Interest ◽  
Significant Performance ◽  
Cmos Ic ◽  
Bulk Processing ◽  
Soi Technology

Silicon-On-Insulator (SOI) technology [1-4] has been shown to have significant performance and fabrication advantages over conventional bulk processing for a wide variety of large scale CMOS IC applications. Advantages in radiation environments has generated significant interest in this technology from military and space science communities [5,6]. Possible advantages of SOI technology for low power, low voltage and high performance circuit applications is under serious consideration by several commercial IC manufacturers [7,8].

scholarly journals Analog Low-Voltage Current-Mode Implementation of Digital Logic Gates

2003 ◽  
Vol 26 (2) ◽  
pp. 111-114 ◽  
Author(s):  
Muhammad Taher Abuelma'atti
Keyword(s):  
Power Series ◽  
Low Voltage ◽  
Logic Gates ◽  
Current Mode ◽  
New Technique ◽  
Basic Logic ◽  
Spice Simulation ◽  
Simulation Results ◽  
A New Technique ◽  
Logic Functions

In this letter a new technique is introduced for implementing the basic logic functions using analog current-mode techniques. By expanding the logic functions in power series expressions, and using summers and multipliers, realization of the basic logic functions is simplified. Since no transistors are working in saturation, the problem of fan-out is alleviated. To illustrate the proposed technique, a circuit for simultaneous realization of the logic functions NOT, OR, NAND and XOR is considered. SPICE simulation results, obtained with 3 V supply, are included

The Study of Energy Storage Technology Application in Wind Power Integration

2013 ◽  
Vol 391 ◽  
pp. 261-264
Author(s):  
Xiao Ning Xu ◽  
Xue Song Zhou
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Large Scale ◽  
Low Voltage ◽  
System Stability ◽  
Technology Application ◽  
Wind Power Integration ◽  
Advantages And Disadvantages ◽  
Storage Technology ◽  
Energy Storage Technology

The classification and application range of energy storage technology are briefly introduced. Challenges for large-scale wind power integration are summarized. With regard to the problems in system stability, low voltage ride-through ability of wind the turbine generator, and power quality, the paper elaborated some solutions based on energy storage technology, and analyzed their advantages and disadvantages. With the character of energy storage technology combined, the paper put forward some advice of energy storage technology applying in wind power integration.

scholarly journals Technology Acceptance in Healthcare: A Systematic Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10537
Author(s):  
Adi A. AlQudah ◽  
Mostafa Al-Emran ◽  
Khaled Shaalan
Keyword(s):  
Systematic Review ◽  
Technology Acceptance ◽  
Large Scale ◽  
Empirical Studies ◽  
Influential Factors ◽  
Full Potential ◽  
Eligibility Criteria ◽  
Factors Affecting ◽  
The Usa ◽  
Healthcare Technologies

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

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