The IEEE Brain Initiative eNewsletter is a quarterly online publication launched in January 2017. It features practical and timely information and forward-looking commentary on neurotechnologies and neuroengineering. eNewsletter articles can describe recent breakthroughs in research, primers on methods of interests, or report recent events such as conferences or workshops. You can contact the eNewsletter editor with any questions concerning the topic or content of your article.
Communicated by Dr. Jun Wang
Linran Zhao, Wen Li, Maysam Ghovanloo, Yaoyao Jia
There is an increasing realization that the majority of brain functions relate to a large distributed network of neurons that are spread over different interconnected regions of the brain. Thus, neural recording and modulation of the future will require the ability to simultaneously interface with multiple neural sites distributed over a large brain area. Traditional methods for modulating neuronal function have relied on direct stimulation by tiny electrodes, which effectiveness is undermined by the limited spatial and temporal precision with which individual cells can be selectively targeted. The emergence of optogenetic stimulation provides distinct advantages over electrical stimulation, such as cell-type specificity, sub-millisecond temporal precision, and rapid reversibility. Optogenetic neuromodulation has the potential to revolutionize the study of how neurons operate as members of larger networks and may ultimately help patients suffering from neurological disorders. Hence, we aspire to design a distributed wireless neural interface framework to stimulate large-scale neuronal ensembles over large brain areas. The distributed framework includes an array of tiny, wireless, and highly efficient implants, each of which operates autonomously to stimulate neural activities.
Communicated by Dr. Yiwen Wang
Leilei Gu, Yucheng Ding, Zhiyong Fan
“To see is to believe”. High-performance imaging devices are essential in society, particularly in the current age of Artificial Intelligence (AI) +. The biological eyes have been polished by natural selection for millions of years and their function has been verified by the diverse environment. Learning from the masterpiece of nature is therefore a shortcut to improve our manmade systems. As one of the wisest creatures in nature, human eyes are advanced image sensing systems with superiorities such as high resolution, wide field-of-view (FoV), high energy efficiency, and strong accommodations. Their high performance originates from the combined effect of a vastly flexible optical system, high-density and sensitive photoreceptor arrays, and powerful neural networks from both retina and cortex. The human eyes have a spherical shape with a hemispherical retina. A hemispherical shape matches well with the Petzval surface, which is the theoretical focal plane of the spherical lens, leading to clear and sharp imaging. In regular cameras, to mitigate the mismatching in planar structure, a delicate lens array has to be inserted to gradually bend the focal plane into quasi-flat. In our cell phones, there are 10-16 lens. With a well-designed hemispherical image sensor, high-quality imaging with a simple structure can be achieved.
D. Y. Barsakcioglu, S. Luan, L. Grand, T. G. Constandinou
The era of bioelectronic healthcare is dawning upon us. As electronic systems shrink in size and improve in functionality, we see more and more emerging devices that can track vital signs, such as heart rate and blood pressure, realising the grand vision of highly connected sensor nodes monitoring patients’ health beyond the hospital doors. The real revolution in digital healthcare, however, lies in bringing not only the diagnostics but also the therapy to the patient which requires interfacing the world of electronics with biology.
A New Method for Generating Realistic Null Data Exploiting Underlying Graph Structure with Application to EEG
E. Pirondini, A. Vybornova, M. Coscia, and D. Van De Ville
Technological and computational advances are making available large amounts of high-dimensional and rich-structured biomedical data, including brain images and signals. Acknowledging the network structure in our analyses opens a multitude of avenues in investigating “systems level” properties. For instance, computational neuroscience has boosted the interest in modeling and analyzing large datasets using concepts normally applied in networks and graph theories.
R. Chavarriaga, C. Carey, C. Tom, B. Ash
The field of Brain-Machine Interfacing (BMI) is going through a very exciting period where the state-of- the-art in research is currently being tested on its intended end-users. Evidently, this translation from laboratory proof-of concepts to viable clinical and assistive solutions entails a large set of challenges. Furthermore, the possibility of deploying and commercializing BMI-based solutions requires researchers, manufacturers, and regulatory agencies to ensure these devices comply with well-defined criteria on their safety and effectiveness. In consequence, there is an increased interest on development of appropriate standards for BMI systems.