Biology subjects

Büschges, A.

Publications and source records attributed to Büschges, A..

3 recordsLinked to original sources

bioRxiv2023

Natural variability and individuality of walking behavior in Drosophila

Insects use walking behavior in a large number of contexts, such as exploration, foraging, escape and pursuit, or migration. A lot is known on how nervous systems produce this behavior in general and also how certain parameters vary with regard to walking direction or speed, for instance. An aspect that has not received much attention is if and how walking behavior varies across individuals of a particular species. To address this, we created a large corpus of kinematic walking data of many individuals of the fruit fly Drosophila. We only selected instances of straight walking in a narrow range of walking speeds to minimize the influence of these high-level parameters, aiming to uncover more subtle aspects of variability. Using high-speed videography and automated annotation we captured the positions of the six leg tips for thousands of steps and used principal components analysis to characterize the postural space individuals used during walking. Our analysis shows that the largest part of walking kinematics can be described by five principal components (PCs). Separation of these five PCs into a 2-dimensional and a 3-dimensional subspace was found to divide the description of walking behavior into invariant features shared across individuals and features that relate to the specifics of individuals; the latter features can be regarded as idiosyncrasies. We also demonstrate that this approach can detect the effects of experimental interventions in an unbiased manner and that general aspects of individuality, such as the average width of the individual walking posture, can be described. Summary statementThe kinematics of walking behavior in Drosophila can be decomposed into general aspects of motor output and idiosyncrasies of individual flies.

animal behavior and cognition↗

bioRxiv2022

Load Non-Linearly Modulates Movement Reflex Gain in an Insect Leg via a Distributed Network of Identified Nonspiking Interneurons

Producing context-specific motor acts requires sensorimotor neural networks to integrate multiple sensory modalities. Some of this integration occurs via presynaptic interactions between proprioceptive afferent neurons themselves,1,2 other by afferents of different modalities targeting appropriate motor neurons (MNs).3-5 How the interneuronal network typically interposed between sensory afferents and MNs contributes to this integration, particularly at single-neuron resolution, is much less understood. In stick insects, this network contains nonspiking interneurons (NSIs) converging onto the posture-controlling slow extensor tibiae motor neuron (SETi). We analyzed how load altered movement signal processing by tracing the interaction of movement (femoral chordotonal organ, fCO) and load (tibial campaniform sensilla, tiCS) signals from the afferents through the NSI network to the motor output. On the afferent level, load reduced movement signal gain by presynaptic inhibition; tiCS stimulation elicited primary afferent depolarization and reduced fCO afferent action potential amplitude. In the NSI network, graded responses to movement and load inputs summed nonlinearly and increased the gain of NSIs opposing movement-induced reflexes. The gain of SETi and muscle movement reflex responses consequently decreased. Gain modulation was movement parameter-specific and required presynaptic inhibition; pharmacologically blocking presynaptic inhibition abolished load-dependent tuning of SETi responses. These data describe sensorimotor gain control at the sensory, premotor, and motor levels. Presynaptic inhibition-mediated nonlinear integration allowed the NSI network to respond to movement sensory input in a context (load)-dependent manner. These findings show how gain changes can allow premotor networks to integrate multiple sensory modalities and thus generate context-appropriate motor activity.

neuroscience↗

bioRxiv2022

Network architecture producing swing to stance transitions in an insect walking system

The walking system of the stick insect is one of the most thoroughly described invertebrate systems. We know a lot about the role of sensory input in the control of stepping of a single leg. However, the neuronal organization and connectivity of the central neural networks underlying the rhythmic activation and coordination of leg muscles still remain elusive. It is assumed that these networks can couple in the absence of phasic sensory input due to the observation of spontaneous recurrent patterns (SRPs) of coordinated motor activity equivalent to fictive stepping-phase transitions. Here we sought to quantify the phase of motor activity within SRPs in the isolated and interconnected meso- and metathoracic ganglia. We show that SRPs occur not only in the meso-, but also in the metathoracic ganglia of the stick insect, discovering a qualitative difference between them. We construct a network based on neurophysiological data capable of reproducing the measured SRP phases to investigate this difference. By comparing network output to the biological measurements we confirm the plausibility of the architecture and provide a hypothesis to account for these qualitative differences. The neural architecture we present couples individual central pattern generators to reproduce the fictive stepping-phase transitions observed in deafferented stick insect preparations after pharmacological activation, providing insights into the neural architecture underlying coordinated locomotion.

neuroscience↗