• Media type: E-Article
  • Title: Optogenetic Stimulation of Human Neural Networks Using Fast Ferroelectric Spatial Light Modulator—Based Holographic Illumination
  • Contributor: Schmieder, Felix [Author]; Klapper, Simon D. [Author]; Koukourakis, Nektarios [Author]; Busskamp, Volker [Author]; Czarske, Jürgen W. [Author]
  • imprint: Basel : Molecular Diversity Preservation International (MDPI), [2018]
  • Language: German
  • DOI: 10.3390/app8071180
  • ISSN: 2076-3417
  • RVK notation: ZG 1100 : Zeitschriften
  • Keywords: Technische Universität Dresden ; Technik ; Publishing Fund ; computergeneriertes Hologramm ; Publikationsfond ; computer-generated hologram ; ferroelectric liquid crystal ; optogenetics ; Optogenetik ; technology ; ferroelektrischer Flüssigkristall
  • Origination:
  • Footnote: Hinweis: Link zum Artikel, der zuerst in der Zeitschrift 'Applied Sciences' erschienen ist. DOI: 10.3390/app8071180
  • Description: The generation and application of human stem-cell-derived functional neural circuits promises novel insights into neurodegenerative diseases. These networks are often studied using stem-cell derived random neural networks in vitro, with electrical stimulation and recording using multielectrode arrays. However, the impulse response function of networks is best obtained with spatiotemporally well-defined stimuli, which electrical stimulation does not provide. Optogenetics allows for the functional control of genetically altered cells with light stimuli at high spatiotemporal resolution. Current optogenetic investigations of neural networks are often conducted using full field illumination, potentially masking important functional information. This can be avoided using holographically shaped illumination. In this article, we present a digital holographic illumination setup with a spatial resolution of about 8 µm, which suffices for the stimulation of single neurons, and offers a temporal resolution of less than 0.6 ms. With this setup, we present preliminary single-cell stimulation recording of stem-cell derived induced human neurons in a random neural network. This will offer the opportunity for further studies on connectivity in such networks.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)