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. 2007 Jun 12;104(24):10229-33.
doi: 10.1073/pnas.0703697104. Epub 2007 Jun 5.

Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons

Affiliations

Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons

Hermann Cuntz et al. Proc Natl Acad Sci U S A. .

Abstract

Complex flight maneuvers require a sophisticated system to exploit the optic flow resulting from moving images of the environment projected onto the retina. In the fly's visual course control center, the lobula plate, 10 so-called vertical system (VS) cells are thought to match, with their complex receptive fields, the optic flow resulting from rotation around different body axes. However, signals of single VS cells are unreliable indicators of such optic flow parameters in the context of their noisy, texture-dependent input from local motion measurements. Here we propose an alternative encoding scheme based on network simulations of biophysically realistic compartmental models of VS cells. The simulations incorporate recent data about the highly selective connectivity between VS cells consisting of an electrical axo-axonal coupling between adjacent cells and a reciprocal inhibition between the most distant cells. We find that this particular wiring performs a linear interpolation between the output signals of VS cells, leading to a robust representation of the axis of rotation even in the presence of textureless patches of the visual surround.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Overview of the VS cell network. (A) Suggested connectivity scheme from ref. . Adjacent cells are coupled electrically and distal cells inhibit each other (schematic filled circles). (B) Ten VS cells as obtained from two-photon image stacks for which detailed compartmental models were reconstructed. Cells were placed manually according to their position in the lobula plate with neighboring dendritic arborizations slightly overlapping.
Fig. 2.
Fig. 2.
Connecting the VS network. (A–C) Ca2+ imaging in VS1. (A) Raw fluorescence image. (B) Relative change of fluorescence (ΔF/F) when injecting current in VS1 primary dendrite. (C) ΔF/F when injecting current in VS2 primary dendrite. (D) Potential distribution in the VS1 model when current is injected into the primary dendrite of VS2. (E) Potential responses at primary dendrite in VS2–10 after current injection of −10 nA in VS1 primary dendrite. Black line, model; red line, experimental counterpart. (F) Voltage transfer from all different VS cells at the location of the synapses in the axon terminal. Normalized amplitudes; dotted horizontal line represents 0 mV. Arrows indicate location of current injection for the different traces. (G) Potential response in a false-color spatial distribution throughout the VS network when injecting 10 nA in VS1 primary dendrite. The color scale saturates at 3 mV.
Fig. 3.
Fig. 3.
Visual response in the VS network. (A) Attenuation along the axon of the visual response (black) and during current injection at the primary dendrite (red) in the model. Standard deviations among the different VS cells are represented by the shaded areas. (B) Potential attenuation halfway down the axon in the model (red) and from double recordings (black). (C) Dendritic responses of the individual VS cells along the horizontal axis of the visual field with the chosen distribution of dendritic synapses. Colors are as in Fig. 1. (D) Receptive field measured in the primary dendrite of a VS5 model in the connected (black) and unconnected (red) network. (E) Same as C, but measured in the axon terminal. (F) Receptive field measured in the axon terminal of a VS5 model in the connected network before (black) and after (red) simulated ablation of the neighboring VS6 cell.
Fig. 4.
Fig. 4.
Coupling improves detection of the center of rotation. (A) An artificial image rotating clockwise around its center. A snapshot of the visual flow-field as determined from a two-dimensional motion detector array is superimposed. The rotational structure is clearly visible. (B) Same as A, but using a natural image (taken from the natural image database, Hans van Hateren, http://hlab.phys.rug.nl/imlib/index.html). The flow-field structure is much less homogenous. (C and D) Visual response in the VS network model when the vertical vector components of the flow-field are fed into the respective VS dendrite synapses for both the artificial and the natural image. Black squares indicate unprocessed signals in the dendrites; red circles indicate axonal outputs. (E and F) VS cell with minimum absolute potential during the rotation of both images around 360°. Black line, potentials at the dendrites; red line, potentials at the axon.

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