IEEE Organizations related to Membrane Potentials

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Conferences related to Membrane Potentials

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (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 papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 21st International Conference on Vacuum Electronics (IVEC)

Technical presentations will range from the fundamental physics of electron emission and modulated electron beams to the design and operation of devices at UHF to THz frequencies, theory and computational tool development, active and passive components, systems, and supporting technologies.System developers will find that IVEC provides a unique snapshot of the current state-of-the-art in vacuum electron devices. These devices continue to provide unmatched power and performance for advanced electromagnetic systems, particularly in the challenging frequency regimes of millimeter-wave and THz electronics.Plenary talks will provide insights into the history, the broad spectrum of fundamental physics, the scientific issues, and the technological applications driving the current directions in vacuum electronics research.


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2019 IEEE 19th International Conference on Nanotechnology (IEEE-NANO)

DNA Nanotechnology Micro-to-nano-scale Bridging Nanobiology and Nanomedicine Nanoelectronics Nanomanufacturing and Nanofabrication Nano Robotics and Automation Nanomaterials Nano-optics, Nano-optoelectronics and Nanophotonics Nanofluidics Nanomagnetics Nano/Molecular Heat Transfer & Energy Conversion Nanoscale Communication and Networks Nano/Molecular Sensors, Actuators and Systems


2019 IEEE 9th International Nanoelectronics Conferences (INEC)

Topics of Interests (but not limited to)• Application of nanoelectronic• Low-dimensional materials• Microfluidics/Nanofluidics• Nanomagnetic materials• Carbon materials• Nanomaterials• Nanophotonics• MEMS/NEMS• Nanoelectronic• Nanomedicine• Nano Robotics• Spintronic devices• Sensor and actuators• Quality and Reliability of Nanotechnology


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Periodicals related to Membrane Potentials

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No periodicals are currently tagged "Membrane Potentials"


Most published Xplore authors for Membrane Potentials

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Xplore Articles related to Membrane Potentials

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Simulation Studies of Neuronal Modulation Using Magneto-electric Nanoparticles for Astrocyte Stimulation

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019

A computational study of the effects of using magneto-electric nanoparticles (MNPs) to modulate brain neurons by stimulating astrocytes is discussed in this paper. The MNP brain-stimulation approach in the blood-brain barrier enables large-scale micrometer-level neuromodulation that leads to a specific stimulation pathway and avoids lateral effect. We describe here a simulation of nanoparticle stimulation used to modulate the neurons of ...


XOR learning by spiking neural network with infrared communications

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018

A Spiking Neural Network (SNN), which expresses information by spike trains, has an ability to process information with low energy like a human brain. Hardware implementation of a SNN is an important research problem. If the neurons are linked by wireless communications, SNNs can obtain the spatial degree of freedom, which may extend application area dramatically. Additionally, such SNNs can ...


Cell-type Selective Stimulation of Neurons Based on Single Neuron Models

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019

Selectively stimulating neuron types within the brain can enable new treatment possibilities for neurological disorders and feedback in brain-machine interfaces. Prior computational work has shown that by choosing a sinusoidal signal with appropriate amplitude and frequency, one can obtain one- directional stimulation, i.e., one can stimulate a mammalian inhibitory neuron without stimulating an excitatory neuron. However, bidirectional selectivity is not ...


Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

2018 14th IEEE International Conference on Signal Processing (ICSP), 2018

In pulse-based neural networks, synaptic dynamics can have direct influence on learning of neural codes, and encoding of spatiotemporal spike patterns. In this paper, we propose an adaptive synapse circuit for increased flexibility and efficacy of signal processing units in neuromorphic structures. The synapse acts as a multi-layer computational network, and includes multi- compartment dendrites and different types of post-synaptic ...


Study on the Breakdown of Cell Membrane and Vacuolar Membrane by Electric Pulses

2018 International Electrical Engineering Congress (iEECON), 2018

This paper presents the application of electric field to induce the breakdown of plant cells in a microfluidic device. The device was used to constrict electric field through an orifice and enhance the membrane charging process. Different behaviors of membrane breakdown observed in the experiments were discussed.


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Educational Resources on Membrane Potentials

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IEEE-USA E-Books

  • Simulation Studies of Neuronal Modulation Using Magneto-electric Nanoparticles for Astrocyte Stimulation

    A computational study of the effects of using magneto-electric nanoparticles (MNPs) to modulate brain neurons by stimulating astrocytes is discussed in this paper. The MNP brain-stimulation approach in the blood-brain barrier enables large-scale micrometer-level neuromodulation that leads to a specific stimulation pathway and avoids lateral effect. We describe here a simulation of nanoparticle stimulation used to modulate the neurons of a hypothetical patient with Parkinson's disease. The major characterized symptoms of the PD patients under this simulation are tremor. Our simulation studies indicate the pulsed sequences of the electric field in an affected neuron could be raised to levels comparable to those of healthy people, leading to the possibility of this novel brain modulation for treating this symptom of Parkinson's disease.

  • XOR learning by spiking neural network with infrared communications

    A Spiking Neural Network (SNN), which expresses information by spike trains, has an ability to process information with low energy like a human brain. Hardware implementation of a SNN is an important research problem. If the neurons are linked by wireless communications, SNNs can obtain the spatial degree of freedom, which may extend application area dramatically. Additionally, such SNNs can process information with low energy, owing to wireless communication by the spike trains. Therefore, it is regarded as low power-consumption wireless sensor networks (WSNs) with adding the functions of SNN neurons to wireless sensor nodes. This “Wireless Neural Sensor Networks” can distribute information processing like a brain on the WSN nodes. This paper presents a SNN with infrared(IR) communications as the first step of the above concept. Neurons are implemented by field programmable gate array, which are linked by IR communications. The implemented SNN succeeded in acquiring the XOR function through reinforcement learning.

  • Cell-type Selective Stimulation of Neurons Based on Single Neuron Models

    Selectively stimulating neuron types within the brain can enable new treatment possibilities for neurological disorders and feedback in brain-machine interfaces. Prior computational work has shown that by choosing a sinusoidal signal with appropriate amplitude and frequency, one can obtain one- directional stimulation, i.e., one can stimulate a mammalian inhibitory neuron without stimulating an excitatory neuron. However, bidirectional selectivity is not achievable using just sinusoidal inputs. In this work, which is also computational, we design novel current waveforms to achieve this bidirectional selectivity. To do so, we explicitly exploit the non-linearity of neuronal membrane potential in response to stimulating currents. These current waveforms are able to stimulate either of the two neuron-types without stimulating the other. Further, we are also able to design a waveform which stimulates both neurons. Moreover, we can ensure a relatively high firing rate (~100 Hz) when a neuron-type is targeted for stimulation.

  • Multi-Layer Neuromorphic Synapse for Reconfigurable Networks

    In pulse-based neural networks, synaptic dynamics can have direct influence on learning of neural codes, and encoding of spatiotemporal spike patterns. In this paper, we propose an adaptive synapse circuit for increased flexibility and efficacy of signal processing units in neuromorphic structures. The synapse acts as a multi-layer computational network, and includes multi- compartment dendrites and different types of post-synaptic back propagating signals. With built-in temporal control mechanisms, the resulting reconfigurable network allows the implementation of synaptic homeostatics.

  • Study on the Breakdown of Cell Membrane and Vacuolar Membrane by Electric Pulses

    This paper presents the application of electric field to induce the breakdown of plant cells in a microfluidic device. The device was used to constrict electric field through an orifice and enhance the membrane charging process. Different behaviors of membrane breakdown observed in the experiments were discussed.

  • Dynamic Systems Approach to Improve the Design of a Phenomenological Analog Neuron Circuit

    Silicon neuron circuits emulate the electrophysiological behavior of real neurons and conductances. A detailed model mapped onto silicon neuron can be beneficial to the improvement of circuit design. Here we present a dynamic systems approach to obtain a detailed mathematical model describing the dynamics of a biophysically realistic silicon neuron. The approximate analytic solution of its firing rate fits simulation data from our neuron chip fabricated using a 130 nm CMOS process very well. Meanwhile, transistors that contribute critically to variation of firing activities are discovered, which helps the improvement of neuron mismatch.

  • Mimicking Gait Pattern Generators

    To replicate the behavior of (large scale) biologically inspired networks through neuromorphic circuits the question arises on how to model the synaptic interconnections between neurons. Biological representations often use unidirectional couplings between neurons which waive problems on electrical models, as electrical networks in general are not free from feedback. We propose a simple solution based on a four port circulator to overcome this issue. The method involves attaching appropriate resistances to this circulator, depending on the desired coupling. The wave digital representation of this approach reveals that our modeling enables unidirectional and free from feedback interconnections between neurons. We use the proposed method to investigate multiple gait pattern generators of a four-legged animal.

  • A Flow-Through Device for Simultaneous Dielectrophoretic Trapping and AC Electroporation

    Cell isolation and selected transfection of those isolated cells is integral to single-cell analyses platforms. We present a scalable platform for simultaneous negative dielectrophoretic trapping and AC electroporation of cells and experimentally validate that cells can experience both phenomena simultaneously in a controlled manner. The geometry is scalable for single- cells. This has the potential to address two primary problems facing microscale electroporation: cell proximity to coplanar electrodes and material proximity to porated cells.

  • An Optimized Morris-Lecar Neuron Model Using Wave Digital Principles

    Neuromorphic circuits are seen as a potential candidate to mimic the behavior of biological networks. Hardware realizations of specific components, like memristive devices, in such networks, are hard to find for the desired functionality. We propose an emulation technique that enables parameter optimization on a digital model, which can be utilized as a guideline to support hardware realizations and reduce development efforts. The method is based on digitally replicating the Morris-Lecar neuron model via the wave digital concept. It should be emphasized that our approach is applicable to single devices, subparts of the circuit or the complete circuit. Although we focus on small-scale neuromorphic circuits in this work, our procedure is also suitable to emulate large-scale applications in a reasonable amount of time.

  • Computational Characterization of the Cellular Origins of Electroencephalography*

    Despite the widespread use of Electroecephalography (EEG) as an imaging modality, neural generators of current dipoles measured by EEG at the scalp are not fully understood. Here, we use two morphologically accurate multicompartments neuron models (layer IV pyramidal cell and layer V spiny stellate cell) to characterize how spiking neurons generate current dipoles in response to synaptic input. The simulations indicate that the dipole generated by synaptic inputs required to drive a pyramidal cell to threshold is smaller than the dipole associated the action potential itself. These results suggest a greater role of spiking neural activity toward EEG signals measured at the scalp than typically assumed.



Standards related to Membrane Potentials

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