Conferences related to Neural Activity

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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 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)

The world's premiere conference in MEMS sensors, actuators and integrated micro and nano systems welcomes you to attend this four-day event showcasing major technological, scientific and commercial breakthroughs in mechanical, optical, chemical and biological devices and systems using micro and nanotechnology.The major areas of activity in the development of Transducers solicited and expected at this conference include but are not limited to: Bio, Medical, Chemical, and Micro Total Analysis Systems Fabrication and Packaging Mechanical and Physical Sensors Materials and Characterization Design, Simulation and Theory Actuators Optical MEMS RF MEMS Nanotechnology Energy and Power


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Periodicals related to Neural Activity

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


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 I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Neural Activity

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Xplore Articles related to Neural Activity

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Modeling Nitric Oxide Induced Neural Activity and Neurovascular Coupling in a Cerebellum Circuit

2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018

Functional magnetic resonance imaging (fMRI) allows to identify brain regions activated during rest, normal and diseased conditions attributing a function or a task to microcircuit activity. In order to understand signals from fMRI techniques, bottom-up modeling would be needed to reconstruct the neurovascular coupling attributing neural activity to local blood flow changes. In this paper, we present a bottom-up mathematical ...


An Adaptive Nonlinear Regularized Observer for Neural Activity Reconstruction

2018 IEEE ANDESCON, 2018

In this paper, a novel method for neural activity reconstruction based on an adaptive non-linear regularized observer is proposed. The regularized observer is based on a discrete nonlinear state space system that describe homogeneous activity into the brain by considering a physiologically meaningful model. In order to obtain an adequately performance of the nonlinear state equation, the parameters of the ...


Non-Linear Iterative Regularization Algorithm for Neural Activity Reconstruction from EEG Data

2018 IEEE ANDESCON, 2018

In this work a novel non-linear iterative regularization algorithm applied to the reconstruction of neuronal activity is presented. A physiologically-based non-linear spatio-temporal constraint is used for solving the dynamic inverse problem associated to the reconstruction of neural activity of the distributed sources. The proposed method includes the spatio-temporal constraint in a cost function based on a l -2 norm. A ...


Suppression of Neural Activity with High Frequency Stimulation

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

Deep brain stimulation (DBS) has been shown to generate suppression of abnormal neural activity in patients with Parkinson's disease and epilepsy. High frequency stimulation is applied to the brain through depth electrodes in the range of 50 to 200 Hz. Yet the mechanisms underlying the suppression effect have not yet been elucidated. In order to study directly the effect of ...


3D Visual Discomfort Predictor: Analysis of Disparity and Neural Activity Statistics

IEEE Transactions on Image Processing, 2015

Being able to predict the degree of visual discomfort that is felt when viewing stereoscopic 3D (S3D) images is an important goal toward ameliorating causative factors, such as excessive horizontal disparity, misalignments or mismatches between the left and right views of stereo pairs, or conflicts between different depth cues. Ideally, such a model should account for such factors as capture ...


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Educational Resources on Neural Activity

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IEEE.tv Videos

Q&A with Dr. Jennifer Gelinas: IEEE Brain Podcast, Episode 8
Roozbeh Ghaffari of MC10 accepts the IEEE Spectrum Emerging Technology Award - Honors Ceremony 2016
Sean Sliger of Neuropace accepts the IEEE Spectrum Technology in the Service of Society Award - Honors Ceremony 2016
Development of Neural Interfaces for Robotic Prosthetic Limbs
Local Activity, Memristor, and 137 - Leon Chua: 2016 International Conference on Rebooting Computing
Auditory Neural Pathway Simulation - IEEE Rebooting Computing 2017
ICASSP 2010 - Advances in Neural Engineering
Day 2 Welcome - Promise Activity: Making a Difference - WIE ILC 2018
Achieving Swarm Intelligence with Spiking Neural Oscillators - IEEE Rebooting Computing 2017
20 Years of Neural Networks: A Promising Start, A brilliant Future- Video contents
Wind Power: The Technology
Towards On-Chip Optical FFTs for Convolutional Neural Networks - IEEE Rebooting Computing 2017
Improved Deep Neural Network Hardware Accelerators Based on Non-Volatile-Memory: the Local Gains Technique: IEEE Rebooting Computing 2017
Lizhong Zheng's Globecom 2019 Keynote
On the Physical Underpinnings of the Unusual Effectiveness of Probabilistic and Neural Computation - IEEE Rebooting Computing 2017
Large-scale Neural Systems for Vision and Cognition
IEEE Collabratec: How to Manage Your Settings
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Artificial Neural Networks, Intro
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing

IEEE-USA E-Books

  • Modeling Nitric Oxide Induced Neural Activity and Neurovascular Coupling in a Cerebellum Circuit

    Functional magnetic resonance imaging (fMRI) allows to identify brain regions activated during rest, normal and diseased conditions attributing a function or a task to microcircuit activity. In order to understand signals from fMRI techniques, bottom-up modeling would be needed to reconstruct the neurovascular coupling attributing neural activity to local blood flow changes. In this paper, we present a bottom-up mathematical modeling of neuro- vascular coupling in rat cerebellum granule cells. Granule cells are numerous and main source for NO production in cerebellum. While matching experimental estimations, the effect of cerebellar blood flow and neural activity, a model of nitric oxide (NO) in the cerebellar granular was simulated and the diffusion, production and consumption of NO at the synapse and the contribution for neuro vascular coupling was modeled. This paper showcases the first step attributing cerebellum sub-molecular changes to clinical observations reconstructing neural activity mappings to population responses when combined with BOLD modeling.

  • An Adaptive Nonlinear Regularized Observer for Neural Activity Reconstruction

    In this paper, a novel method for neural activity reconstruction based on an adaptive non-linear regularized observer is proposed. The regularized observer is based on a discrete nonlinear state space system that describe homogeneous activity into the brain by considering a physiologically meaningful model. In order to obtain an adequately performance of the nonlinear state equation, the parameters of the non-linear model are also estimated by using a multivariate non-linear least squares estimator resulting in an adaptive non-linear observer. Considering the complexity of the model and the large amount of states to be estimated, an iterative solution of the non-linear adaptive observer is proposed based on an IRA-L2 representation with spatial basis. A comparison of the performance of the algorithm in terms of relative error is analyzed, and also the evolution of the parameters is considered. A simulation framework based on the solution of a continuous non-linear differential equation that describe the neural activity in each source is used to evaluate against the multiple sparse priors method.

  • Non-Linear Iterative Regularization Algorithm for Neural Activity Reconstruction from EEG Data

    In this work a novel non-linear iterative regularization algorithm applied to the reconstruction of neuronal activity is presented. A physiologically-based non-linear spatio-temporal constraint is used for solving the dynamic inverse problem associated to the reconstruction of neural activity of the distributed sources. The proposed method includes the spatio-temporal constraint in a cost function based on a l -2 norm. A simulated EEG data-set is used in order to evaluate the performance of the proposed algorithm for three and five simultaneously active sources under several signal-to-noise ratios, by using relative error measurement. A comparison analysis is performed against the MSP and IRA-L2 source reconstruction methods, where the proposed non-linear method improves the reconstruction of neural activity in terms of the relative error.

  • Suppression of Neural Activity with High Frequency Stimulation

    Deep brain stimulation (DBS) has been shown to generate suppression of abnormal neural activity in patients with Parkinson's disease and epilepsy. High frequency stimulation is applied to the brain through depth electrodes in the range of 50 to 200 Hz. Yet the mechanisms underlying the suppression effect have not yet been elucidated. In order to study directly the effect of HFS in the brain, sinusoidal stimulation was applied in the in-vitro brain slice preparation. Sinusoidal stimulation was chosen in order to observe the activity during the stimulation by filtering the stimulation artifact. Sinusoidal stimulation at 50 Hz applied to the CA1 region of the hippocampus was observed to block epileptiform activity in three separate models of epilepsy induced by low-calcium, high potassium and picrotoxin (GABA<sub>A</sub> blocker). Stimulation applied to the alveus showed that activity in both the cell bodies (evoked potentials) and in the axons (compound action potentials) is suppressed. The frequency range of this effect is nearly identical to that of DBS with maximum suppression effect between 50 and 200 Hz. The effect could not be attributed to desynchronization or damage and was associated with increased extracellular potassium concentrations. These data provide new insights into the effects of HFS on neuronal elements and show that HFS can block axonal activity through non-synaptic mechanisms

  • 3D Visual Discomfort Predictor: Analysis of Disparity and Neural Activity Statistics

    Being able to predict the degree of visual discomfort that is felt when viewing stereoscopic 3D (S3D) images is an important goal toward ameliorating causative factors, such as excessive horizontal disparity, misalignments or mismatches between the left and right views of stereo pairs, or conflicts between different depth cues. Ideally, such a model should account for such factors as capture and viewing geometries, the distribution of disparities, and the responses of visual neurons. When viewing modern 3D displays, visual discomfort is caused primarily by changes in binocular vergence while accommodation in held fixed at the viewing distance to a flat 3D screen. This results in unnatural mismatches between ocular fixations and ocular focus that does not occur in normal direct 3D viewing. This accommodation vergence conflict can cause adverse effects, such as headaches, fatigue, eye strain, and reduced visual ability. Binocular vision is ultimately realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. Realizing that the neuronal responses are directly implicated in both the control and experience of 3D perception, we have developed a model-based neuronal and statistical framework called the 3D visual discomfort predictor (3D-VDP) that automatically predicts the level of visual discomfort that is experienced when viewing S3D images. 3D-VDP extracts two types of features: 1) coarse features derived from the statistics of binocular disparities and 2) fine features derived by estimating the neural activity associated with the processing of horizontal disparities. In particular, we deploy a model of horizontal disparity processing in the extrastriate middle temporal region of occipital lobe. We compare the performance of 3D-VDP with other recent discomfort prediction algorithms with respect to correlation against recorded subjective visual discomfort scores, and show that 3D-VDP is statistically superior to the other methods.

  • Resource Optimized Processor for Real-Time Neural Activity Monitoring

    Closing the loop between living tissues and electronics is at the basis of the treatment of numerous pathologies or disabilities with prosthetic devices: artificial pancreas, neuroprostheses, BCI, etc. Biosignals are acquired and processed to detect a signature that finally controls actuators (insulin delivery pump, electrical stimulator, etc.). The in vivo application of such a paradigm implies severe constraints on speed and consumption at all loop processing stages. In the research described in this paper, we optimized computation in the first stage of an acquisition system dedicated to closed- loop living/artificial experiments. The described functions are wavelet-based spike detection and slow signal filtering. We focus here on the optimization of these algorithms to reduce hardware resources while keeping a strong constraint on the computation time, and easy scalability for massive multichannel recordings. The architecture is implemented on FPGA for prototyping and evaluation with living cells recorded on a 60 multi-electrode array. We describe the minimal requirements of the algorithms, present the computation architecture, and the required hardware resources, as well as the evolution of these resources depending on the number of recording channels.

  • Live demonstration: In-vivo imaging of neural activity with dynamic vision sensors

    The demonstration shows the comparison of two novel Dynamic and Active Pixel Vision Sensors (DAVIS) in the context of a simulated neural imaging experiment. The first sensor, the SDAVIS, has, although a lower resolution (188×192) with respect to the previous generation of DAVIS sensors, 10X higher temporal contrast sensitivity. The second sensor, BSIDAVIS, combines a higher resolution (346×260) with a higher light sensitivity (quantum efficiency) because of its Back Side Illumination (BSI) manufacturing.

  • Influence of Stimulus Complexity on the Properties of Neural Activity During Perceptual Process

    This work is devoted to study of the features of time-frequency EEG structure, related to visual information processing in the brain. As a concrete example, the influence of stimulus complexity on the time-frequency EEG properties is considered. It is shown that an increase of visual stimulus complexity leads to an increase in the response amplitude of the neural network of the brain in the parietal and occipital cortex. It is associated with the excitation of the neural center of visual attention.

  • Image Reconstruction from Neural Activity Recorded from Monkey Inferior Temporal Cortex Using Generative Adversarial Networks

    The generative adversarial network (GAN) is a powerful image generation machine learning model. Several lines of research have shown that GAN is applicable to brain-machine interface technology for deciphering human brain activity, such as EEG and fMRI signals, to visualize what human observers see during recording. However, although current GAN models can synthesize photorealistic images, the quality and variety of image reconstruction from brain activity data recorded by non-invasive techniques are still limited. In this study, we recorded neural spike activities in monkey brain using microelectrode arrays implanted directly on the surface of the inferior temporal cortex, a brain area crucial for visual object recognition. The recorded data were then inputted into a state-of-the-art GAN model (Dosovitskiy & Brox, 2016 [1]) to reconstruct images viewed by the monkey during the experiments. The results showed the advantage of invasive recording methods over non-invasive methods for improving the quality of image reconstruction. The results also demonstrated that the proposed decoding approach is useful in neuroscience research to explore and visualize information represented in the recoding site.

  • NATAS: Neural Activity Trace Aware Saliency

    Saliency detection has raised much interest in computer vision recently. Many visual saliency models have been developed for individual images, video clips, and image pairs. However, image sequence, one most general occasion in the real world, is not explored yet. A general image sequence is different from video clips whose temporal continuity is maintained and image pairs where common objects exist. It might contain some similar low-level properties while completely distinct contents. Traditional saliency detection methods will fail on these general sequences. Based on this consideration, this paper investigates the shortcomings of the classical saliency detection methods, which significantly limit their advantages: 1) inability to capture the natural connections among sequential images, 2) over-reliance on motion cues, and 3) restriction to image pairs/videos with common objects. In order to address these problems, we propose a framework that performs the following contributions: 1) construct an image data set as benchmark through a rigorously designed behavioral experiment, 2) propose a neural activity trace aware saliency model to capture the general connections among images, and 3) design a novel measure to handle the low-level clues contained among sequential images. Experimental results demonstrate that the proposed saliency model is associated with a tremendous advancement compared with traditional methods when dealing with the general image sequence.



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