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Draft:Gabriele Scheler

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  • Comment: The amount of resubmissions makes me question if you have read the comments left by Cabrils. Since this draft is a biography of a living person, you really would want to cite most claims with a reliable source if you want this article to pass, especially at "Early life and education" section where there is just a single Youtube link. ABG (Talk/Report any mistakes here) 09:57, 10 July 2024 (UTC)
  • Comment: Well done on creating the draft, and it may potentially meet the relevant requirements (including WP:GNG, WP:ANYBIO, WP:NPROF) but presently it is not clear that it does.
    As you may know, Wikipedia's basic requirement for entry is that the subject is notable. Essentially subjects are presumed notable if they have received significant coverage in multiple published secondary sources that are reliable, intellectually independent of each other, and independent of the subject. To properly create such a draft page, please see the articles ‘Your First Article’, ‘Referencing for Beginners’ and ‘Easier Referencing for Beginners’.
    Please note that many of the references are written by the subject (Scheler) rather than about her. TO establish notability 9as defined) requires reliable sources about her.
    Additionally, the draft tends to read too much like a CV, which Wikipedia is not.
    Also, if you have any connection to the subject, including being paid, you have a conflict of interest that you must declare on your Talk page (to see instructions on how to do this please click the link).
    Please familiarise yourself with these pages before amending the draft. If you feel you can meet these requirements, then please make the necessary amendments before resubmitting the page. It would help our volunteer reviewers by identifying, on the draft's talk page, the WP:THREE best sources that establish notability of the subject.
    It would also be helpful if you could please identify with specificity, exactly which criteria you believe the page meets (eg "I think the page now meets WP:NPROF criteria #3, because XXXXX").
    You may also wish to leave a note for me on my talk page and I would be happy to reassess. As I said, I do think this draft has potential so please do persevere. Cabrils (talk) 03:35, 27 January 2024 (UTC)

Gabriele Dorothea Scheler (born 1960 in Göttingen) is a German-American computer scientist and neuroscientist. Her main contribution to neuroscience is a theory of neuroplasticity, which uses internal memory to guide adaptivity at the membrane[1]. She is co-founder of the Carl Correns Foundation for Mathematical Biology.[2], a non-profit institute to further research and scholarship in mathematics applied to biology. The institute was founded in 2011, and went into operation in 2016. It was named after her great-grandfather Carl Erich Correns who pioneered the application of mathematical and statistical tools for theoretical discovery. It has gained reputation for research on neuroplasticity, pioneering a major new theory for molecular memory beyond the electrophysiological theories of LTP/LTD. She discussed her biography and the topics of AI, NeuroAI and genAI in an interview [3] in 2024.

Early life and education[edit]

Gabriele Scheler grew up in Göttingen, as the daughter of Fritz Scheler, and Elisabeth Scheler née Correns, the daughter of Carl Wilhelm Correns, who had a formative influence on his granddaughter. Her family includes Erich Correns (chemist), Carl Erich Correns, Erich Correns (artist), de:Emil Ballowitz, Hugo Karl Anton Pernice, Erich Pernice, Werner Scheler, Max Scheler. She graduated from de:Theodor-Heuss-Gymnasium_(Göttingen) as valedictorian three years early in 1977. After a year at the University of Tübingen, she moved to the Institute for logic and scientific theory at the LMU Munich, where she also did her Ph.D[3]. Gabriele Scheler suffers from the consequences of a brain trauma, which she received in her early twenties, caused by a two-week coma in Berkeley 1983, probably induced by deliberate poisoning. A doctoral scholarship for Stanford University had to be declined because of this sudden illness. Her experience as a patient contributed to her resolve to investigate computational neuroscience problems with a view of later medical applications. She married in 1991 and had a son in 1995. Since 1998 she has lived in California, first in San Diego, and then in Mountain View.

Career[edit]

Scheler pioneered neural network research[4] of linguistic semantics and grammatical categories.[5], and co-edited the first book on ML in NLP[6]. While at Wilfried Brauer's group together with Sepp Hochreiter, she developed a novel approach for classification based on adaptive distance measures[4], later taken up by Yann LeCun and his group. [7][8]. She moved to the Salk Institute in 1998, where she worked on topics such as dopamine and neuromodulation[9], neuronal synchronization, intrinsic plasticity and cell-internal protein signaling. At UC Berkeley (2001-2004), she collaborated with the Redwood Neuroscience Institute. From 2005 until 2011, she organized the Biological Modelling Club at Stanford, a dedicated group of researchers in mathematical and computational biology. There she invented a method for calculating dose-response matrices in protein signaling pathways with applications for drug development[10]

She investigated lognormal distributions of intrinsic frequencies and synaptic strengths, and published work on localist memory[11], together with Johann Schumann. Most significantly, she pioneered a new theory of neural plasticity ("there is room on the inside"), which is a significant advance since the Hebbian synaptic plasticity theory of LTP/LTD ("Neurons that fire together, wire together").[1]

References[edit]

  1. ^ a b Gabriele Scheler (Sept. 2022) Sketch of a novel approach to a neural model. arxiv 2209.06865 [1]
  2. ^ Carl Correns Foundation for Mathematical Biology [2]]
  3. ^ a b Hitech NeuroAI[[3]]
  4. ^ a b Gabriele Scheler [4] Feature Selection with Exception Handling-An Example from Phonology, 1992.
  5. ^ Gabriele Scheler (1995). [5] Generating English plural determiners from semantic representations: A neural network learning approach. DOI:10.1007/3-540-60925-3_38 Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing.
  6. ^ Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing.
  7. ^ https://cs.nyu.edu/~yann/talks/lecun-20070914-ipam-1.pdf
  8. ^ Chopra, S.; Hadsell, R.; Lecun, Y. (2005). "Learning a Similarity Metric Discriminatively, with Application to Face Verification". 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. pp. 539–546. doi:10.1109/CVPR.2005.202. ISBN 0-7695-2372-2.
  9. ^ Gabriele Scheler [6] Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance.
  10. ^ Gabriele Scheler (Sept 2013) Determination of output of biochemical reaction networks. Patent: US 20130246019 A1
  11. ^ Gabriele Scheler, Johann Schumann [7] Localist plasticity identified by mutual information. DOI: 10.1101/658153. 2019/2024
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