A central steering circuit in Drosophila
Feng, K.; Khan, M.; Minegishi, R.; Mueller, A.; Van De Poll, M. N.; Swinderen, B. v.; Dickson, B. J.
Show abstract
Locomotion steering control enables animals to pursue targets, evade threats, avoid obstacles, and explore their environment. Steering commands are generated in the brain and communicated via descending neurons to leg or wing motor circuits. The diversity of ways in which turns are triggered and executed has led to the view that steering might rely on distributed neural processing across multiple control circuits. Here, however, we present evidence for a central steering circuit in Drosophila that is used for both goal-directed and exploratory turns and is capable of eliciting turns ranging from subtle course corrections to rapid saccades. The circuit is organized in a hierarchy, the top layer of which comprises the reciprocally connected DNa03 and LAL013 neurons. Our data suggest that turns are initiated by DNa03 neurons and reinforced and stabilized through a winner-take-all mechanism involving LAL013. The descending DNa11 neurons form an intermediate layer. They receive input from both DNa03 and LAL013 and target leg motor circuits directly as well as indirectly through subordinate descending neurons. DNa11 activation coordinately changes the stepping directions of all six legs to generate rapid saccadic turns. Together, these data define a central steering control circuit in Drosophila that is flexibly used to generate turns as the fly exploits or explores its environment.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Combined patterns of activity of major neuronal classes underpin a global change in brain state during spontaneous and forced walk in Drosophila 98%
- Inhibitory circuits control leg movements during Drosophila grooming 97%
- Extensive and diverse patterns of cell death sculpt neural networks in insects. 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.