Understanding the control algorithms that underlie the optomotor response in larval zebrafish

What is it about?

In most animals, vision is central to the control of locomotory behaviors. In particular, innate motor responses to movement of the ground image across the retina - "optic flow" - allow flying animals to react to gusts of air and fish to changes in water current. As fish are carried downstream, for example, movement of the riverbed image elicits forward movement against the stream in bouts (short periods of swimming followed by rest periods), a behavior known as the optomotor response. The optomotor response has typically been assumed to stabilize the fish's position and prevent it from drifting downstream. Here, we combined behavioral experiments and computational modeling to elucidate the underlying control algorithms and explore in detail the relationship between visual stimuli and the initiation and speed of swim bouts. Surprisingly, we find that the degree of stabilization is only partial and varies systematically with height above ground, raising questions about the function of this behavior. We describe several novel findings and present a computational model proposing two separate processes that underlie bout initiation and bout speed, which jointly predict mean swim speed and degrees of regulation.

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Photo by Timothy L Brock on Unsplash

Photo by Timothy L Brock on Unsplash

Why is it important?

Zebrafish have emerged as a key model system for investigating the neural mechanisms underlying sensorimotor behaviors in vertebrates. The extensive genetic studies in this species facilitate the use of a wide range of optogenetic techniques, while their small size and translucency early in life provide a unique opportunity for in-vivo studies of the interplay between sensory input, whole brain activity and motor output. As one of the earliest and most robust visual behaviors to develop, the optomotor response has been the focus of many recent neurobiological studies. However, the behavior itself and its underlying control algorithms have received relatively little attention. This research addresses this gap and sheds new light on the underlying dynamics of this behavior, providing cruical insights for future work aimed at identifying its neural basis.

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