1. Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents

    This article has 5 authors:
    1. Joel Kowalewski
    2. Sean M Boyle
    3. Ryan Arvidson
    4. Jadrian Ejercito
    5. Anandasankar Ray
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      This useful study uses a chemoinformatics pipeline to identify a list of candidate mosquito repellants that may be pleasant to smell and safe for humans. The strength of evidence and in particular the computational methodology are incomplete because it is insufficiently benchmarked against other leading models. At the high concentrations tested, there may also be off-target effects of the repellents on the mosquitoes that are not considered.

    Reviewed by eLife

    This article has 9 evaluationsAppears in 1 listLatest version Latest activity
  2. Understanding neural circuit principles for representation learning through joint-embedding predictive architectures

    This article has 3 authors:
    1. Ashena Gorgan Mohammadi
    2. Manu Srinath Halvagal
    3. Friedemann Zenke
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      This manuscript proposes a valuable idea on how cortical networks may learn a helpful representation of sensory stimuli. The model implementing this idea is tested in multiple experimental paradigms. However, the evidence remains incomplete as to whether the method supports both invariance and equivariance and whether it can estimate the dynamics of the moving object.

    Reviewed by eLife

    This article has 5 evaluationsAppears in 1 listLatest version Latest activity
  3. Predicting functional topography of the human visual cortex from cortical anatomy at scale

    This article has 11 authors:
    1. Fernanda L Ribeiro
    2. Robert Satzger
    3. Felix Hoffstaedter
    4. Christian Bürger
    5. Peer Herholz
    6. David Linhardt
    7. Noah C Benson
    8. D Samuel Schwarzkopf
    9. Alexander M Puckett
    10. Steffen Bollmann
    11. Martin N Hebart
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      This valuable study presents a tool that uses brain anatomy to predict the layout and size of early visual maps, and it is strengthened by the use of a large and diverse collection of scans to examine differences across people and groups. The evidence is solid for the general usefulness of the approach, but incomplete for some of the broader claims about prediction accuracy and use across data sets, particularly for estimates of map size and for showing that the model improves on repeated functional measurements. This paper is likely to be of significant interest to visual perception researchers, especially those who use fMRI.

    Reviewed by eLife

    This article has 5 evaluationsAppears in 1 listLatest version Latest activity
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