Neuroscience

Drosophila learn to prefer immobile spherical objects through repeated physical interaction

1. Kenichi Iwasaki
2. Sena Kawano
3. Ashnu Cassod
4. Charles Neuhauser
5. Aleksandr Rayshubskiy

Reviewed by PREreview

The resource elasticity of control

1. Levi Solomyak
2. Aviv Emanuel
3. Eran Eldar

• Curated by eLife

  eLife Assessment

  This study makes the valuable claim that people track, specifically, the elasticity of control (that is, the degree to which outcome depends on how many resources - such as money - are invested), and that control elasticity is impaired in certain types of psychopathologies. A novel task is introduced that provides solid evidence that this learning process occurs and that human behavior is sensitive to changes in the elasticity of control. Evidence that elasticity inference is distinct from more general learning mechanisms and is related to psychopathology remains incomplete.

Reviewed by eLife

Intrinsic and circuit mechanisms of predictive coding in a grid cell network model

1. Inayath Shaikh
2. Collins Assisi

• Curated by eLife

  eLife Assessment

  This valuable study presents a mechanistic model of predictive coding by medial entorhinal cortex grid cells, implemented with biologically detailed conductance-based neurons. The evidence supporting the emergence of this coding scheme from specific membrane currents and the anatomical connectivity among inhibitory neurons is solid. However, the justification for the choice of connectivity patterns and other network parameters remains somewhat incomplete. This work will be of interest to neuroscientists working on spatial navigation, circuit dynamics, and neuronal coding.

Reviewed by eLife

An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster

1. Jessica L Zung
2. Shigehiro Namiki
3. Geoffrey W Meissner
4. Han SJ Cheong
5. Marta Costa
6. Katharina Eichler
7. Tomke Stürner
8. Gregory SXE Jefferis
9. Claire Managan
10. FlyLight Project Team
11. Wyatt Korff
12. Gwyneth M Card

• Curated by eLife

  eLife Assessment

  Complementing previous work (Namiki et al, 2018), this study provides an important resource for the Drosophila community as it reports 500 lines targeting descending neurons (DN), in addition to compiling 306 existing DN lines from the literature. The compelling work characterizes 146 DNs and makes a critical link with the DNs identified in Electron microscopy (EM). The lines in this paper will be of interest to Drosophila neuroscientists who will be able to use the reported genetic drivers for further functional characterization of DNs and circuit mapping in conjunction with existing EM datasets.

Reviewed by eLife