Motor neurons tune premotor activity in a vertebrate central pattern generator

journal articleCentral patterns generators (CPGs) are neural circuits that drive rhythmic motor output without sensory feedback. Vertebrate CPGs are generally believed to operate in a top-down manner in which premotor interneurons activate motor neurons that in turn drive muscles. In contrast, the frog (Xenopus laevis) vocal CPG contains a functionally unexplored neuronal projection from the motor nucleus to the premotor nucleus, indicating a recurrent pathway that may contribute to rhythm generation. In this study, we characterized the function of this bottom-up connection. The X. laevis vocal CPG produces a 50-60 Hz "fast trill" song used by males during courtship. We recorded "fictive vocalizations" in the in vitro CPG from the laryngeal nerve while simultaneously recording premotor activity at the population and single-cell level. We show that transecting the motor-to-premotor projection eliminated the characteristic firing rate of premotor neurons. Silencing motor neurons with the intracellular sodium channel blocker QX-314 also disrupted premotor rhythms, as did blockade of nicotinic synapses in the motor nucleus (the putative location of motor neuron-to-interneuron connections). Electrically stimulating the laryngeal nerve elicited primarily IPSPs in premotor neurons that could be blocked by a nicotinic receptor antagonist. Our results indicate that an inhibitory signal, activated by motor neurons, is required for proper CPG function. To our knowledge, these findings represent the first example of a CPG in which precise premotor rhythms are tuned by motor neuron activity

A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms

An Image-Free Opto-Mechanical System for Creating Virtual Environments and Imaging Neuronal Activity in Freely Moving Caenorhabditis elegans

Non-invasive recording in untethered animals is arguably the ultimate step in the analysis of neuronal function, but such recordings remain elusive. To address this problem, we devised a system that tracks neuron-sized fluorescent targets in real time. The system can be used to create virtual environments by optogenetic activation of sensory neurons, or to image activity in identified neurons at high magnification. By recording activity in neurons of freely moving C. elegans, we tested the long-standing hypothesis that forward and reverse locomotion are generated by distinct neuronal circuits. Surprisingly, we found motor neurons that are active during both types of locomotion, suggesting a new model of locomotion control in C. elegans. These results emphasize the importance of recording neuronal activity in freely moving animals and significantly expand the potential of imaging techniques by providing a mean to stabilize fluorescent targets

Larval Zebrafish Lateral Line as a Model for Acoustic Trauma

Excessive noise exposure damages sensory hair cells, leading to permanent hearing loss. Zebrafish are a highly tractable model that have advanced our understanding of drug-induced hair cell death, yet no comparable model exists for noise exposure research. We demonstrate the utility of zebrafish as model to increase understanding of hair cell damage from acoustic trauma and develop protective therapies. We created an acoustic trauma system using underwater cavitation to stimulate lateral line hair cells. We found that acoustic stimulation resulted in exposure time- and intensity-dependent lateral line and saccular hair cell damage that is maximal at 48-72 h post-trauma. The number of TUNEL+ lateral line hair cells increased 72 h post-exposure, whereas no increase was observed in TUNEL+ supporting cells, demonstrating that acoustic stimulation causes hair cell-specific damage. Lateral line hair cells damaged by acoustic stimulation regenerate within 3 d, consistent with prior regeneration studies utilizing ototoxic drugs. Acoustic stimulation-induced hair cell damage is attenuated by pharmacological inhibition of protein synthesis or caspase activation, suggesting a requirement for translation and activation of apoptotic signaling cascades. Surviving hair cells exposed to acoustic stimulation showed signs of synaptopathy, consistent with mammalian studies. Finally, we demonstrate the feasibility of this platform to identify compounds that prevent acoustic trauma by screening a small redox library for protective compounds. Our data suggest that acoustic stimulation results in lateral line hair cell damage consistent with acoustic trauma research in mammals, providing a highly tractable model for high-throughput genetic and drug discovery studies

Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms

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