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Biology subjects

Lafaye, J.

Publications and source records attributed to Lafaye, J..

2 recordsLinked to original sources

Evolutionary divergence of locomotion in two related vertebrate species

Locomotion exists in diverse forms in nature and is adapted to the environmental constraints of each species1. However, little is known about how closely related species with similar neuronal circuitry can evolve different navigational strategies to explore their environments. We established a powerful approach in comparative neuroethology to investigate evolution of neuronal circuits in vertebrates by comparing divergent swimming pattern of two closely related larval fish species, Danionella translucida (DT) and Danio rerio or zebrafish (ZF)2,3. During swimming, we demonstrate that DT utilizes lower half tail-beat frequency and amplitude to generate a slower and continuous swimming pattern when compared to the burst-and-glide swimming pattern in ZF. We found a high degree of conservation in the brain anatomy between the two species. However, we revealed that the activity of a higher motor region, referred here as the Mesencephalic Locomotion Maintenance Neurons (MLMN) correlates with the duration of swim events and differs strikingly between DT and ZF. Using holographic stimulation, we show that the activation of the MLMN is sufficient to increase the frequency and duration of swim events in ZF. Moreover, we propose two characteristics, availability of dissolved oxygen and timing of swim bladder inflation, which drive the observed differences in the swim pattern. Our findings uncover the neuronal circuit substrate underlying the evolutionary divergence of navigational strategies and how they are adapted to their respective environmental constraints.

neuroscience↗

From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predict the stationary distribution of the fishs body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can similarly capture the statistics of both spontaneous and contrast-driven navigation.

animal behavior and cognition↗