Neuromodulation of Flight Speed Regulation (2014)

Confocal image of a Drosophila brain and ventral nerve cord showing the GFP-labeled octopamine neurons (green), which we genetically silenced with Kir2.1. Red labels neuropil. Image taken by my colleague Marie Suver.
Confocal image of a Drosophila brain and ventral nerve cord showing the GFP-labeled octopamine neurons (green), which we genetically silenced with Kir2.1. Red labels neuropil. Image taken by my colleague Marie Suver.

Recent evidence suggests that flies’ sensitivity to large-field optic flow is increased by the release of octopamine during flight. This increase in gain presumably enhances visually mediated behaviors such as the active regulation of forward speed, a process that involves the comparison of a vision-based estimate of velocity with an internal set point. To determine where in the neural circuit this comparison is made, we selectively silenced the octopamine neurons in the fruit fly Drosophila, and examined the effect on vision-based velocity regulation in free-flying flies. We found that flies with inactivated octopamine neurons accelerated more slowly in response to visual motion than control flies, but maintained nearly the same baseline flight speed. Our results are parsimonious with a circuit architecture in which the internal control signal is injected into the visual motion pathway upstream of the interneuron network that estimates groundspeed.

Coauthored with Marie Suver, Michael Dickinson. Read more at the Journal of Experimental Biology.

Preferred visual motion set point is either modulated by changes in gain identical to those applied in the lobula plate tangential cell (LPTC) network (H2a), or the set point enters the visual sensory-motor cascade upstream of the LPTC network (H2b). (A) Block diagram showing the models under consideration. (B) Gain versus temporal frequency curve used for the low pass filter in the visual sensory system of our model. The data points are drawn from the temporal frequency tuning curve given in fig. 1D in Suver et al. (Suver et al., 2012), which summarizes the responses of electrophysiological recordings of vertical system (VS) cells in response to vertical motion. The original data were scaled such that the gain at a temporal frequency of 1 Hz is 1. The line shows a third-order polynomial fit. Note that this results in a transfer function defined in the linear temporal frequency domain, rather than the oscillatory temporal frequency domain. In order to implement this type of filter in our control model, we calculate the gain based on the linear temporal frequency of the stimulus. (C) Baseline subtracted membrane potential of VS cells in response to a downward 8 Hz visual motion stimulus during flight; data repeated, and magnified, from Suver et al. (Suver et al., 2012). The gray traces show the mean responses each of 19 individual flies, and the bold trace shows the group mean. (D) Model predictions compared with our results from Fig. 3E. The solid blue line shows the model prediction for the parental controls (gain=5.5) with the biomechanical saturation, whereas the dotted blue line shows the prediction without saturation. The solid red line shows the model prediction for the flies with inactivated octopamine neurons (gain=2.2). Note that the models H1, H2a and H2b all give identical acceleration responses. (E) Model predictions (color coded consistently with A) compared with mean velocity versus time responses for parental controls (left) and flies with inactivated octopamine neurons (right). The data traces are repeated from Fig. 3A. Note that H2 is a better fit.
Preferred visual motion set point is either modulated by changes in gain identical to those applied in the lobula plate tangential cell (LPTC) network (H2a), or the set point enters the visual sensory-motor cascade upstream of the LPTC network (H2b). (A) Block diagram showing the models under consideration. (B) Gain versus temporal frequency curve used for the low pass filter in the visual sensory system of our model. The data points are drawn from the temporal frequency tuning curve given in fig. 1D in Suver et al. (Suver et al., 2012), which summarizes the responses of electrophysiological recordings of vertical system (VS) cells in response to vertical motion. The original data were scaled such that the gain at a temporal frequency of 1 Hz is 1. The line shows a third-order polynomial fit. Note that this results in a transfer function defined in the linear temporal frequency domain, rather than the oscillatory temporal frequency domain. In order to implement this type of filter in our control model, we calculate the gain based on the linear temporal frequency of the stimulus. (C) Baseline subtracted membrane potential of VS cells in response to a downward 8 Hz visual motion stimulus during flight; data repeated, and magnified, from Suver et al. (Suver et al., 2012). The gray traces show the mean responses each of 19 individual flies, and the bold trace shows the group mean. (D) Model predictions compared with our results from Fig. 3E. The solid blue line shows the model prediction for the parental controls (gain=5.5) with the biomechanical saturation, whereas the dotted blue line shows the prediction without saturation. The solid red line shows the model prediction for the flies with inactivated octopamine neurons (gain=2.2). Note that the models H1, H2a and H2b all give identical acceleration responses.
(E) Model predictions (color coded consistently with A) compared with mean velocity versus time responses for parental controls (left) and flies with inactivated octopamine neurons (right). The data traces are repeated from Fig. 3A. Note that H2 is a better fit.