Name
Title | ||
|---|---|---|
Maximally Efficient Prediction In The Early Fly Visual System May Support Evasive Flight Maneuvers |
Abstract | ||
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The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly's future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.Author summary Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system's processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1371/journal.pcbi.1008965 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 17 | 5 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siwei Wang | 1 | 40 | 13.72 |
| Idan Segev | 2 | 153 | 27.18 |
| Alexander Borst | 3 | 0 | 0.34 |
| Stephanie E. Palmer | 4 | 0 | 1.35 |