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    Item type:Publication, Access status: with files ,
    Interfacing with the Brain: How Nanotechnology Can Contribute
    (American Chemical Society (ACS), 2025-03-25)
    Ahmed, Abdullah A.A.
    ;
    Alegret, Nuria
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    Almeida, Bethany
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    Alvarez-Puebla, Ramón
    ;
    Andrews, Anne M.
    Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors’ expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.
    Publicationtype: Review Article
      36  55
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    Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation
    (2023-05-26)
    Rustige, Lennart
    ;
    Kummer, Janis
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    Griese, Florian
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    Borras, Kerstin
    ;
    Brüggen, Marcus
    Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein generative adversarial networks (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple fully connected neural network benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a convolutional neural network can be improved slightly. However, this is not the case for a vision transformer.
    Publicationtype: Journal Article
      15  11
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    Item type:Publication, Access status: without files ,
    Radio Galaxy Classification with wGAN-Supported Augmentation
    (2022-09)
    Kummer, Janis
    ;
    Rustige, Lennart
    ;
    Griese, Florian
    ;
    Borras, Kerstin
    ;
    Brüggen, Marcus
    Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.
    Publicationtype: Conference Paper
      16