IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005268.html
   My bibliography  Save this article

Could a Neuroscientist Understand a Microprocessor?

Author

Listed:
  • Eric Jonas
  • Konrad Paul Kording

Abstract

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.Author Summary: Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used naïvely, fall short of producing a meaningful understanding.

Suggested Citation

  • Eric Jonas & Konrad Paul Kording, 2017. "Could a Neuroscientist Understand a Microprocessor?," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-24, January.
  • Handle: RePEc:plo:pcbi00:1005268
    DOI: 10.1371/journal.pcbi.1005268
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005268&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005268?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. Misha B. Ahrens & Jennifer M. Li & Michael B. Orger & Drew N. Robson & Alexander F. Schier & Florian Engert & Ruben Portugues, 2012. "Brain-wide neuronal dynamics during motor adaptation in zebrafish," Nature, Nature, vol. 485(7399), pages 471-477, May.
    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. Roy Harpaz & Minh Nguyet Nguyen & Armin Bahl & Florian Engert, 2021. "Precise visuomotor transformations underlying collective behavior in larval zebrafish," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    2. Ziyue Wang & Xiang Fei & Xiaotong Liu & Yanjie Wang & Yue Hu & Wanling Peng & Ying-wei Wang & Siyu Zhang & Min Xu, 2022. "REM sleep is associated with distinct global cortical dynamics and controlled by occipital cortex," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Shivesh Chaudhary & Sihoon Moon & Hang Lu, 2022. "Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    4. Dániel L. Barabási & Gregor F. P. Schuhknecht & Florian Engert, 2024. "Functional neuronal circuits emerge in the absence of developmental activity," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Jeffrey P Nguyen & Ashley N Linder & George S Plummer & Joshua W Shaevitz & Andrew M Leifer, 2017. "Automatically tracking neurons in a moving and deforming brain," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-19, May.
    6. Johannes Friedrich & Weijian Yang & Daniel Soudry & Yu Mu & Misha B Ahrens & Rafael Yuste & Darcy S Peterka & Liam Paninski, 2017. "Multi-scale approaches for high-speed imaging and analysis of large neural populations," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-24, August.

    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:plo:pcbi00:1005268. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.