Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue

Kjeldsen, H.; Kaiser, Marcus; Whittington, Miles A.

doi:10.1016/j.jneumeth.2015.05.016

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Moreover, although surface‐plasmons are by definition near‐field waves, the desire to control the near‐field properties of other types of waves has drawn considerable attention in recent years. For example, shaping the nonlinear near‐file emitted from nanostructures , near‐field nonlinear on‐axis and volume holography at the second harmonic , acoustic near‐field holography , microwave near‐field holography , and more . The methods and approaches presented in this manuscript may be of interest and applications to these waves, and others, as well.…”

Section: Discussionmentioning

confidence: 99%

Surface‐plasmon wavefront and spectral shaping by near‐field holography

Epstein

Tsur

Arie

2016

Laser & Photonics Reviews

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Surface‐plasmon‐polariton waves are two‐dimensional electromagnetic surface waves that propagate at the interface between a metal and a dielectric. These waves exhibit unusual and attractive properties, such as high spatial confinement and enhancement of the optical field, and are widely used in a variety of applications, such as sensing and subwavelength optics. The ability to precisely control the spatial and spectral properties of the surface‐plasmon wave is required in order to support the growing interest in both research and applications of plasmonic waves, and to bring it to the next level. Here, we review the challenges and methods for shaping the wavefront and spectrum of plasmonic waves. In particular, we present the recent advances in plasmonic spatial and spectral shaping, which are based on the realization of plasmonic holograms for the optical nearfield.

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Section: Discussionmentioning

confidence: 99%

Surface‐plasmon wavefront and spectral shaping by near‐field holography

Epstein

Tsur

Arie

2016

Laser & Photonics Reviews

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“…This is possible thanks to the simultaneous decomposition of all dimensions of the data, which ensures the same number of IMFs containing the information in the same frequency ranges (Rehman and Mandic, 2010 ). Thus, thanks to the advantages of EMD algorithms over classic linear analysis, they are being increasingly used in neuronal analysis (Liang et al, 2005 ; Huang et al, 2013 ; Al-Subari et al, 2015 ; Alegre-Cortés et al, 2016 ), and they are helping us to achieve a better understanding of the oscillatory properties of neuronal activity (Buzsáki and Draguhn, 2004 ).…”

Section: Synergy Between Emds Machine Learning and Brain Processesmentioning

confidence: 99%

“…To facilitate the analysis of relevant T–F information using ML analysis, we propose to use Empirical Mode Decomposition data-driven algorithms (Huang et al, 1998 , EMD) to extract the relevant T–F features to be studied. This procedure is widely used in signal analysis and has been proved successfully in the analysis of electrophysiological data (Li, 2006 ; Huang et al, 2013 ; Hu and Liang, 2014 ; Al-Subari et al, 2015 ; Alegre-Cortés et al, 2016 ); nevertheless, they have not yet become of common use and are sparsely found in neuroscience publications. As a result, we still use linear and stationary techniques that are unavoidably biasing and blurring relevant information, since they are not able to accurately depict the intermittency and non-linearity of the data.…”

Section: Introductionmentioning

confidence: 99%

Toward an Improvement of the Analysis of Neural Coding

Alegre-Cortés

Soto-Sánchez

Albarracín

et al. 2018

Front. Neuroinform.

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Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

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Holography applications toward medical field: An overview

Haleem

Javaid

Khan

2020

Indian J Radiol Imaging

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Purpose: 3D Holography is a commercially available, disruptive innovation, which can be customised as per the requirements and is supporting Industry 4.0. The purpose of this paper is to study the potential applications of 3D holography in the medical field. This paper explores the concept of holography and its significant benefits in the medical field. Methods: The paper is derived through the study of various research papers on Holography and its applications in the medical field. The study tries to identify the direction of research &development and see how this innovative technology can be used effectively for better treatment of patients. Results: Holography uses digital imaging inputs and provides an extensive visualisation of the data for training doctors, surgeons and students. Holography converts information about the body into a digital format and has the potential to inform, promote and entertain the medical students and doctors. However, it needs a large amount of space for data storage and extensive software support for analysis and skills for customising. This technology seems good to solve a variety of medical issues by storing and using patient data in developing 3D holograms, which are useful to assist successful treatment and surgery. It seems useful in providing flexible solutions in the area of medical research. Finally, the paper identifies 13 significant applications of this technology in the medical field and discusses them appropriately. Conclusion: The paper explores holographic applications in medical research due to its extensive capability of image processing. Holographic images are non-contact 3D images having a large field of depth. A physician can now zoom the holographic image for a better view of the medical part. This innovative technology can create advancements in the diagnosis and treatment process, which can improve medical practice. It helps in quick detection of problems in various organs like brain, heart, liver, kidney etc. By using this technology, medical practitioners can see colourful organs at multiple angles with better accuracy. It opens up an innovative way of planning, testing of procedures and diagnosis. With technological developments, compact hardware and software are now available to help medical research and related applications.

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Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue

Cited by 3 publications

References 39 publications

Surface‐plasmon wavefront and spectral shaping by near‐field holography

Surface‐plasmon wavefront and spectral shaping by near‐field holography

Toward an Improvement of the Analysis of Neural Coding

Holography applications toward medical field: An overview

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