IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34073-3.html
   My bibliography  Save this article

Sensitivity and spectral control of network lasers

Author

Listed:
  • Dhruv Saxena

    (Imperial College London)

  • Alexis Arnaudon

    (Imperial College London
    Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech)

  • Oscar Cipolato

    (Imperial College London)

  • Michele Gaio

    (Imperial College London)

  • Alain Quentel

    (Imperial College London)

  • Sophia Yaliraki

    (Imperial College London)

  • Dario Pisignano

    (NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore
    Università di Pisa)

  • Andrea Camposeo

    (NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore)

  • Mauricio Barahona

    (Imperial College London)

  • Riccardo Sapienza

    (Imperial College London)

Abstract

Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation.

Suggested Citation

  • Dhruv Saxena & Alexis Arnaudon & Oscar Cipolato & Michele Gaio & Alain Quentel & Sophia Yaliraki & Dario Pisignano & Andrea Camposeo & Mauricio Barahona & Riccardo Sapienza, 2022. "Sensitivity and spectral control of network lasers," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34073-3
    DOI: 10.1038/s41467-022-34073-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34073-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34073-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michael T. Schaub & Jean-Charles Delvenne & Renaud Lambiotte & Mauricio Barahona, 2019. "Multiscale dynamical embeddings of complex networks," LIDAM Reprints CORE 3037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Michele Gaio & Dhruv Saxena & Jacopo Bertolotti & Dario Pisignano & Andrea Camposeo & Riccardo Sapienza, 2019. "A nanophotonic laser on a graph," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    3. Schaub, Michael T. & Lehmann, Jörg & Yaliraki, Sophia N. & Barahona, Mauricio, 2014. "Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution," Network Science, Cambridge University Press, vol. 2(1), pages 66-89, April.
    4. Shi Gu & Fabio Pasqualetti & Matthew Cieslak & Qawi K. Telesford & Alfred B. Yu & Ari E. Kahn & John D. Medaglia & Jean M. Vettel & Michael B. Miller & Scott T. Grafton & Danielle S. Bassett, 2015. "Controllability of structural brain networks," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maria Isabel García-Planas & Maria Victoria García-Camba, 2022. "Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach," Mathematics, MDPI, vol. 10(3), pages 1-13, January.
    2. Tadić, Bosiljka & Chutani, Malayaja & Gupte, Neelima, 2022. "Multiscale fractality in partial phase synchronisation on simplicial complexes around brain hubs," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Ociepka, Michał & Kałamała, Patrycja & Chuderski, Adam, 2022. "High individual alpha frequency brains run fast, but it does not make them smart," Intelligence, Elsevier, vol. 92(C).
    4. Huili Sun & Rongtao Jiang & Wei Dai & Alexander J. Dufford & Stephanie Noble & Marisa N. Spann & Shi Gu & Dustin Scheinost, 2023. "Network controllability of structural connectomes in the neonatal brain," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. S. Parker Singleton & Andrea I. Luppi & Robin L. Carhart-Harris & Josephine Cruzat & Leor Roseman & David J. Nutt & Gustavo Deco & Morten L. Kringelbach & Emmanuel A. Stamatakis & Amy Kuceyeski, 2022. "Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Zurrin, Riley & Wong, Samantha Tze Sum & Roes, Meighen M. & Percival, Chantal M. & Chinchani, Abhijit & Arreaza, Leo & Kusi, Mavis & Momeni, Ava & Rasheed, Maiya & Mo, Zhaoyi & Goghari, Vina M. & Wood, 2024. "Functional brain networks involved in the Raven's standard progressive matrices task and their relation to theories of fluid intelligence," Intelligence, Elsevier, vol. 103(C).
    7. Dian Lyu & Shruti Naik & David K. Menon & Emmanuel A. Stamatakis, 2022. "Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Jin, Yi & Zhang, Qingyuan & Chen, Yunxia & Lu, Zhendan & Zu, Tianpei, 2023. "Cascading failures modeling of electronic circuits with degradation using impedance network," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. Richard F Betzel & Katherine C Wood & Christopher Angeloni & Maria Neimark Geffen & Danielle S Bassett, 2019. "Stability of spontaneous, correlated activity in mouse auditory cortex," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    10. Guilherme Ramos & Sérgio Pequito, 2020. "Generating complex networks with time-to-control communities," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-12, August.
    11. Kravitz, H. & Brio, M. & Caputo, J.-G., 2023. "Localized eigenvectors on metric graphs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 214(C), pages 352-372.
    12. Bruton, Oliver J., 2021. "Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron," Intelligence, Elsevier, vol. 86(C).
    13. Elizabeth L. Johnson & Jack J. Lin & David King-Stephens & Peter B. Weber & Kenneth D. Laxer & Ignacio Saez & Fady Girgis & Mark D’Esposito & Robert T. Knight & David Badre, 2023. "A rapid theta network mechanism for flexible information encoding," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    14. Singh, Priti & Chakraborty, Abhishek & Manoj, B.S., 2017. "Link Influence Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 701-713.
    15. Franz Kaiser & Philipp C. Böttcher & Henrik Ronellenfitsch & Vito Latora & Dirk Witthaut, 2022. "Dual communities in spatial networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    16. Atsushi Kikumoto & Apoorva Bhandari & Kazuhisa Shibata & David Badre, 2024. "A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34073-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.