Highlights d LPLC2 and LC4 are the primary direct visual inputs to the giant fiber (GF) d The GF sums LPLC2 and LC4 input to drive escape from looming d LPLC2-GF input encodes looming size, whereas LC4-GF input encodes looming speed d A model summing looming size and speed optical variables reproduces GF responses

An approaching predator and self-motion towards an object can generate similar looming patterns on the retina, but these situations demand different rapid responses. How central circuits flexibly process visual cues to activate appropriate, fast motor pathways remains unclear. Here, we identify two descending neuron (DN) types that control landing and contribute to visuomotor flexibility in Drosophila . For each, silencing impairs visually-evoked landing, activation drives landing, and spike rate determines leg extension amplitude. Critically, visual responses of both DNs are severely attenuated during non-flight periods, effectively decoupling visual stimuli from the landing motor pathway when landing is inappropriate. The flight-dependence mechanism differs between DN types. Octopamine exposure mimics flight effects in one, whereas the other likely receives neuronal feedback from flight motor circuits. Thus, this sensorimotor flexibility arises from distinct mechanisms for gating action-specific descending pathways, such that sensory and motor networks are coupled or decoupled according to the behavioral state.

SummaryBackgroundLimb movements are generally driven by active muscular contractions working with and against passive forces arising in muscles and other structures. In relatively heavy limbs, the effects of gravity and inertia predominate, whereas in lighter limbs, passive forces intrinsic to the limb are of greater consequence. The roles of passive forces generated by muscles and tendons are well understood, but there has been little recognition that forces originating within joints themselves may also be important, and less still that these joint forces may be adapted through evolution to complement active muscle forces acting at the same joint.ResultsWe examined the roles of passive joint forces in insect legs with different arrangements of antagonist muscles. We first show that passive forces modify actively generated movements of a joint across its working range, and that they can be sufficiently strong to generate completely passive movements that are faster than active movements observed in natural behaviors. We further demonstrate that some of these forces originate within the joint itself. In legs of different species adapted to different uses (walking, jumping), these passive joint forces complement the balance of strength of the antagonist muscles acting on the joint. We show that passive joint forces are stronger where they assist the weaker of two antagonist muscles.ConclusionsIn limbs where the dictates of a key behavior produce asymmetry in muscle forces, passive joint forces can be coadapted to provide the balance needed for the effective generation of other behaviors.

Ache JM, Dürr V. Encoding of near-range spatial information by descending interneurons in the stick insect antennal mechanosensory pathway.

No abstract

Much like visually impaired humans use a white-cane, nocturnal insects and mammals use antennae or whiskers for near-range orientation. Stick insects, for example, rely heavily on antennal tactile cues to find footholds and detect obstacles. Antennal contacts can even induce aimed reaching movements. Because tactile sensors are essentially one-dimensional, they must be moved to probe the surrounding space. Sensor movement is thus an essential cue for tactile sensing, which needs to be integrated by thoracic networks for generating appropriate adaptive leg movements. Based on single and double recordings, we describe a descending neural pathway comprising three identified ON-and OFF-type neurons that convey complementary, unambiguous, and short-latency information about antennal movement to thoracic networks in the stick insect. The neurons are sensitive to the velocity of antennal movements across the entire range covered by natural movements, regardless of movement direction and joint angle. Intriguingly, none of them originates from the brain. Instead, they descend from the gnathal ganglion and receive input from antennal mechanoreceptors in this lower region of the CNS. From there, they convey information about antennal movement to the thorax. One of the descending neurons, which is additionally sensitive to substrate vibration, feeds this information back to the brain via an ascending branch. We conclude that descending interneurons with complementary tuning characteristics, gains, input and output regions convey detailed information about antennal movement to thoracic networks. This pathway bypasses higher processing centers in the brain and thus constitutes a shortcut between tactile sensors on the head and the thorax.

Ache JM, Matheson T. Passive resting state and history of antagonist muscle activity shape active extensions in an insect limb.

Many animals, including humans, rely on active tactile sensing to explore the environment and negotiate obstacles, especially in the dark. Here, we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks. We studied the nocturnal stick insect Carausius morosus, a model organism for the study of adaptive locomotion, including tactually mediated reaching movements. Like mammals, insects need to move their tactile sensors for probing the environment. Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast, adaptive motor responses. Our model explains how proprioceptive information about motion and position of the antennae, the main tactile sensors in insects, can be encoded by a single type of mechanosensory afferents. Moreover, it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons (DINs). First, we quantified responses of a DIN population to changes in antennal position, motion and direction of movement. Using principal component (PC) analysis, we find that only two PCs account for a large fraction of the variance in the DIN response properties. We call the two-dimensional space spanned by these PCs ‘coding-space’ because it captures essential features of the entire DIN population. Second, we model the mechanoreceptive input elements of this descending pathway, a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints. Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input. This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population. This reveals the versatility of the framework for modelling a complete descending neural pathway.