Conferences related to Synapses

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2019 25th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC)

The IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC) is the premier forum for researchers to present their latest findings in the area of asynchronous design.


2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

robotics, intelligent systems, automation, mechatronics, micro/nano technologies, AI,


2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)

The series of BIBE Conferences was initiated in 2000 and is the first of its kind in IEEE inspiringothers to follow its path. The 18th annual IEEE International Conference on Bioinformatics andBioengineering aims at building synergy between Bioinformatics and Bioengineering, twocomplementary disciplines that hold great promise for the advancement of research anddevelopment in complex medical and biological systems, agriculture, environment, publichealth, drug design. Research and development in these two areas are impacting the scienceand technology in fields such as medicine, food production, forensics, etc. by advancingfundamental concepts in molecular biology, by helping us understand living organisms atmultiple levels, by developing innovative implants and bio-prosthetics, and by improving toolsand techniques for the detection, prevention and treatment of diseases.


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Periodicals related to Synapses

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Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems Magazine, IEEE


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


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Xplore Articles related to Synapses

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Multilayer Cross-Point Synapses Using Ga-Sn-O Thin Films for Neural Network

2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK), 2018

We have developed multilayer cross-point synapses using Ga-Sn-O (GTO) thin films for neural networks. Twenty intermediate layer metal lines and ten lower and upper layer metal lines make 400 cross-point synapses integrated on a glass substrate. By continuously applying a constant voltage to the GTO thin films, the current value is changed. As a result, we obtained degradation that can ...


Evaluation of (Bi, La)4Ti3012 Thin Film for Capacitor-Type Synapses

2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK), 2018

We evaluated (Bi, La)4Ti3O12(BLT) thin films for capacitor-type synapses. The ferroelectric thin films were formed by a sol-gel method. The hysteresis characteristics of the ferroelectric thin films were evaluated using a Sawyer- Tower-Circuit. The results show that the larger the film thickness of the ferroelectric thin film, the more useful it is as synapses.


FPGA-based training and recalling system for memristor synapses

2016 International Conference on Electronics, Information, and Communications (ICEIC), 2016

Nanoscale memristors can be used as synapses in brain-mimicking neuromorphic systems. To act as synapses, memristors should be programmed or trained for the target synaptic weight values by applying a sequence of voltage pulses. In this paper, we show an implementation of FPGA-based training and recalling system of memristor synapses. Using the implemented FPGA-based training and recalling system of memristor ...


High Performance 2D Perovskite/Graphene Optical Synapses as Artificial Eyes

2018 IEEE International Electron Devices Meeting (IEDM), 2018

Conventional von Neumann architectures feature large power consumptions due to memory wall. Partial distributed architecture using synapses and neurons can reduce the power. However, there is still data bus between image sensor and synapses/neurons, which indicates plenty room to further lower the power consumptions. Here, a novel concept of all distributed architecture using optical synapse has been proposed. An ultrasensitive ...


Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses

2017 IEEE International Electron Devices Meeting (IEDM), 2017

Brain-inspired computing is currently gaining momentum as a viable technology for artificial intelligence enabling recognition, language processing and online unsupervised learning. Brain-inspired circuit design is currently hindered by 2 fundamental limits: (i) understanding the event-driven spike processing in the human brain, and (ii) developing predictive models to design and optimize cognitive circuits. Here we present a comprehensive model for spiking ...


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Educational Resources on Synapses

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IEEE-USA E-Books

  • Multilayer Cross-Point Synapses Using Ga-Sn-O Thin Films for Neural Network

    We have developed multilayer cross-point synapses using Ga-Sn-O (GTO) thin films for neural networks. Twenty intermediate layer metal lines and ten lower and upper layer metal lines make 400 cross-point synapses integrated on a glass substrate. By continuously applying a constant voltage to the GTO thin films, the current value is changed. As a result, we obtained degradation that can be applied to the modified Hebb's learning rule.

  • Evaluation of (Bi, La)4Ti3012 Thin Film for Capacitor-Type Synapses

    We evaluated (Bi, La)4Ti3O12(BLT) thin films for capacitor-type synapses. The ferroelectric thin films were formed by a sol-gel method. The hysteresis characteristics of the ferroelectric thin films were evaluated using a Sawyer- Tower-Circuit. The results show that the larger the film thickness of the ferroelectric thin film, the more useful it is as synapses.

  • FPGA-based training and recalling system for memristor synapses

    Nanoscale memristors can be used as synapses in brain-mimicking neuromorphic systems. To act as synapses, memristors should be programmed or trained for the target synaptic weight values by applying a sequence of voltage pulses. In this paper, we show an implementation of FPGA-based training and recalling system of memristor synapses. Using the implemented FPGA-based training and recalling system of memristor synapses, we compare various pule modulation schemes which can be used in training and recalling memristor synapses. This comparison tells us that the pulse amplitude modulation is more suitable to train memristor synapses precisely than the others.

  • High Performance 2D Perovskite/Graphene Optical Synapses as Artificial Eyes

    Conventional von Neumann architectures feature large power consumptions due to memory wall. Partial distributed architecture using synapses and neurons can reduce the power. However, there is still data bus between image sensor and synapses/neurons, which indicates plenty room to further lower the power consumptions. Here, a novel concept of all distributed architecture using optical synapse has been proposed. An ultrasensitive artificial optical synapse based on a graphene/2D perovskite heterostructure shows very high photo-responsivity up to 730 A/W and high stability up to 74 days. Moreover, our optical synapses has unique reconfigurable light-evoked excitatory/inhibitory functions, which is the key to enable image recognition. The demonstration of an optical synapse array for direct pattern recognition shows an accuracy as high as 80%. Our results shed light on new types of neuromorphic vision applications, such as artificial eyes.

  • Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses

    Brain-inspired computing is currently gaining momentum as a viable technology for artificial intelligence enabling recognition, language processing and online unsupervised learning. Brain-inspired circuit design is currently hindered by 2 fundamental limits: (i) understanding the event-driven spike processing in the human brain, and (ii) developing predictive models to design and optimize cognitive circuits. Here we present a comprehensive model for spiking neural networks based on spike-timing dependent plasticity (STDP) in resistive switching memory (RRAM) synapses. Both a Monte Carlo (MC) model and an analytical model are presented to describe experimental data from a state- of-the-art neuromorphic hardware. The model can predict the learning efficiency and time as a function of the input noise and pattern size, thus paving the way for model-based design of cognitive brain-like circuits.

  • A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural-Network Processor with Cell Current Controlled Writing and Flexible Network Architecture

    This paper presents low-power neural-network (NN) processor using ReRAM to store weights as analog resistance for future AI computing. We propose ReRAM perceptron circuit for realizing large scale integration, highly accurate cell current controlled writing scheme, and flexible network architecture (FNA) in which any NNs can be configured. Fabricated 180nm test chip shows well- controlled analog cell current with linear 30μA dynamic range and 0.59μA variation of 1 sigma, results in 90.8% MNIST numerical recognition rate. Furthermore, 4M synapses integrated 40nm test chip achieves lower analog cell current and 66.5 TOPS/W power efficiency.

  • Effect of delay on the synchronization of weakly coupled neurons via excitatory chemical synapses

    Excitatory chemical coupling connections are ubiquitous in neuronal system. In this paper, we investigate the effect of conduction time delays on synchronization properties of weakly coupled Morris-Lecar neuronal oscillators via excitatory chemical synapses. We first reduce the complex neuronal dynamics to a simple phase model by means of phase-model reduction method. Then we examine the roles of time delays extensively on the synchronization properties by bifurcation analysis and numerical simulation. Finally, we identify the existence and the stability of various phase-locked states, including in-phase, anti-phase and out-of-phase synchronization states.

  • Studying the dynamics of memristive synapses in spiking neuromorphic systems

    The paper describes the numerical study of a simple spiking neuromorphic system comprising three neurons. We consider the Hodgkin-Huxley neuron model represented by equivalent electrical circuit to simulate the dynamical behavior of ionic channels in a cell membrane. The neurons in the system are connected to each other via synapses represented by memristive elements. The applied model of memristive element with two adaptive thresholds allows us to achieve several basic properties of the spike-timing-dependent plasticity (STDP) of biological synapses. The corresponding mathematical model is given as a system of ordinary differential equations. As the experiment findings, we present the results of dynamical analysis of the three-neuron system carried out with extrapolation methods of numerical integration.

  • Digit Recognition Through Unsupervised Learning by Lithium Silicate Synapses

    The spike timing-dependent plasticity (STDP) of synapses has been shown as a fundamental rule of brain learning. In this work, we demonstrate that lithium silicate (LiSiOx) can be used to achieve STDP by exploiting its resistive switching properties. We illustrate that when performing MNIST database recognition the benefit by HRS(high resistance state) variance is utilized. Considering the widely variant HRS is a common phenomenon that many resistors will meet used as synapses, it should be a general applicability in the future development of neuromorphic computing.

  • Evaluation of Letter Reproduction System Using Cellular Neural Network and Oxide Semiconductor Synapses by Logic Simulation

    A letter reproduction system has been developed by a cellular neural network and oxide semiconductor synapses and evaluated by logic simulation. A cellular neural network is utilized because it is suitable for large-scale integration of electronic devices, and oxide semiconductor devices are utilized as synapse elements because the characteristic deterioration can be employed as strength plasticity of synaptic connection with modified Hebbian learning. In this article, first, the structure and operation of the letter reproduction system are explained. Next, it is evaluated by logic simulation, where the dependence of the correction accuracy of the letter reproduction on the variation of deterioration rate of the oxide semiconductor synapses is analyzed.



Standards related to Synapses

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IEEE Standard for Safety Levels With Respect to Human Exposure to Electromagnetic Fields, 0-3 kHz

Develop safety levels for human exposure to electromagnetic fields from 0 to 3kHz. This standard will be based on the results of an evaluation of the relevant scientific literature and proven effects which are well established and for which thresholds of reaction are understood. Field limits will be derived from threshold current densities or internal electric fields.



Jobs related to Synapses

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