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In Vivo Imaging of Neural Activity in Unanesthetized Drosophila Adult Flies

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

Hermann Cuntz¹, Juergen Haag, Friedrich Forstner

¹Wolfson Institute for Biomedical Research, Department of Physiology, University College London, Gower Street, London, United Kingdom. h.cuntz@ucl.ac.uk

Proceedings of the National Academy of Sciences of the United States of America

|June 7, 2007

Summary

Flies use a network of vertical system (VS) cells to interpret optic flow for flight control. This study reveals that their specific wiring enables robust rotation axis detection, even with limited visual information.

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Area of Science:

Neuroscience
Computational Biology
Insect Vision

Background:

Complex flight maneuvers rely on processing optic flow, the apparent motion of the environment on the retina.
In flies, vertical system (VS) cells in the lobula plate are hypothesized to encode rotational optic flow.
Individual VS cell signals are unreliable due to noisy, texture-dependent inputs.

Purpose of the Study:

To investigate an alternative encoding scheme for optic flow using network simulations of VS cells.
To explore how the specific connectivity of VS cells contributes to robust visual processing.

Main Methods:

Biophysically realistic compartmental models of VS cells were simulated.
Network simulations incorporated known connectivity: electrical coupling between adjacent VS cells and reciprocal inhibition between distant VS cells.
The model analyzed the network's ability to represent the axis of rotation.

Main Results:

The specific wiring of VS cells performs a linear interpolation of their output signals.
This network mechanism provides a robust representation of the rotation axis.
The system remains effective even in environments lacking visual texture.

Conclusions:

The interconnected network of VS cells, rather than individual cell outputs, underlies robust optic flow processing.
This computational strategy enhances flight control by providing reliable rotation axis information.
The findings offer insights into neural coding principles in sensory systems.