26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

A kinematic model of stick-insect walking

Animal, and insect walking (locomotion) in particular, have attracted much attention from scientists over many years up to now. The investigations included behavioral, electrophysiological experiments, as well as modeling studies. Despite the large amount of material collected, there are left many unanswered questions as to how walking and related activities are generated, maintained, and controlled. It is obvious that for them to take place, precise coordination within muscle groups of one leg and between the legs is required: intra-and interleg coordination. The nature, the details, and the interactions of these coordination mechanisms are not entirely clear. To help uncover them, we made use of modeling techniques, and succeeded in developing a six-leg model of stick-insect walking. Our main goal was to prove that the same model can mimic a variety of walking-related behavioral modes, as well as the most common coordination patterns of walking just by changing the values of a few input or internal variables. As a result, the model can reproduce the basic coordination patterns of walking: tetrapod and tripod and the transition between them. It can also mimic stop and restart, change from forward-to-backward walking and back. Finally, it can exhibit so-called search movements of the front legs both while walking or standing still. The mechanisms of the model that enable it to produce the aforementioned behavioral modes can hint at and prove helpful in uncovering further details of the biological mechanisms underlying walking

Modelling large scale neuronal networks using 'average neurones'

Large scale neuronal network models have become important tools in studying the information transmission within the CNS. In most cases, these models use simplifying assumptions because of unavailable data (e.g. unknown exact network connectivity), and for technical reasons (to preserve numerical stability of the model). Here, we present a novel approach, based on a probabilistic connectivity principle, to this modelling problem for which no knowledge of the exact network connectivity is required. This principle makes it sufficient to compute only the typical neuronal behaviour, represented by ‘average neurones’, in the network. As a consequence, detailed neurone models can be employed without seriously compromising computational efficiency. Our model thus provides a viable alternative to deterministic models

A putative neuronal network controlling the activity of the leg motoneurons of the stick insect

It is widely accepted that the electrical activity of motoneurons that drive locomotion in the stick insect are controlled by two separate mechanisms: (i) the frequency of the activity through the central pattern generator, which provides the rhythm of movement during locomotion and (ii) the 'magnitude' through circuits distinct from the earlier one. In this study, we show a possible way of how this control mechanism might be implemented in the nervous system of the stick insect by means of a network model. To do this, we had to define the 'magnitude' of the neuronal activity more precisely as the average number of spikes per unit time. The model was constructed on the basis of relevant electrophysiological and morphological data. However, only their integration in the model led to the novel properties that enable the network quickly to adapt the motoneuronal activity to central commands or sensory signals by changing both the firing pattern and intensity of the motoneuron discharges. The network would thus act as the controlling network for each of the muscle pairs that move the individual joints in each of the legs. Our model may contribute to a better understanding of the mechanisms that underlie the fast adaptive control of locomotion in this, and possibly in other types of locomotor systems. NeuroReport 22:943-946 (C) 2011 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins

Dominance of local sensory signals over inter-segmental effects in a motor system: modeling studies

Recent experiments, reported in the accompanying paper, have supplied key data on the impact afferent excitation has on the activity of the levator-depressor motor system of an extremity in the stick insect. The main finding was that, stimulation of the campaniform sensillae of the partially amputated middle leg in an animal where all other but one front leg had been removed, had a dominating effect over that of the stepping ipsilateral front leg. In fact, the latter effect was minute compared to the former. In this article, we propose a local network that involves the neuronal part of the levator-depressor motor system and use it to elucidate the mechanisms that underlie the generation of neuronal activity in the experiments. In particular, we show that by appropriately modulating the activity in the neurons of the central pattern generator of the levator-depressor motor system, we obtain activity patterns of the motoneurons in the model that closely resemble those found in extracellular recordings in the stick insect. In addition, our model predicts specific properties of these records which depend on the stimuli applied to the stick insect leg. We also discuss our results on the segmental mechanisms in the context of inter-segmental coordination

Existence of a Long-Range Caudo-Rostral Sensory Influence in Terrestrial Locomotion

In multisegmented locomotion, coordination of all appendages is crucial for the generation of a proper motor output. In running for example, leg coordination is mainly based on the central interaction of rhythm generating networks, called central pattern generators (CPGs). In slower forms of locomotion, however, sensory feedback, which originates from sensory organs that detect changes in position, velocity and load of the legs' segments, has been shown to play a more crucial role. How exactly sensory feedback influences the activity of the CPGs to establish functional neuronal connectivity is not yet fully understood. Using the female stick insect Carausius morosus, we show for the first time that a long-range caudo-rostral sensory connection exists and highlight that load as sensory signal is sufficient to entrain rhythmic motoneuron (MN) activity in the most rostral segment. So far, mainly rostro-caudal influencing pathways have been investigated where the strength of activation, expressed by the MN activity in the thoracic ganglia, decreases with the distance from the stepping leg to these ganglia. Here, we activated CPGs, producing rhythmic neuronal activity in the thoracic ganglia by using the muscarinic agonist pilocarpine and enforced the stepping of a single, remaining leg. This enabled us to study sensory influences on the CPGs' oscillatory activity. Using this approach, we show that, in contrast to the distance-dependent activation of the protractor-retractor CPGs in different thoracic ganglia, there is no such dependence for the entrainment of the rhythmic activity of active protractor-retractor CPG networks by individual stepping legs.SIGNIFICANCE STATEMENT We show for the first time that sensory information is transferred not only to the immediate adjacent segmental ganglia but also to those farther away, indicating the existence of a long-range caudo-rostral sensory influence. This influence is dependent on stepping direction but independent of whether the leg is actively or passively moved. We suggest that the sensory information comes from unspecific load signals sensed by cuticle mechanoreceptors (campaniform sensilla) of a leg. Our results provide a neuronal basis for the long-established behavioral rules of insect leg coordination. We thus provide a breakthrough in understanding the neuronal networks underlying multilegged locomotion and open new vistas into the neuronal functional connectivity of multisegmented locomotion systems across the animal kingdom.Keywords: CPG; entrainment; inter-segmental coordination; locomotion; six-legged walking

Dominance of local sensory signals over inter-segmental effects in a motor system: experiments

Legged locomotion requires that information local to one leg, and inter-segmental signals coming from the other legs are processed appropriately to establish a coordinated walking pattern. However, very little is known about the relative importance of local and inter-segmental signals when they converge upon the central pattern generators (CPGs) of different leg joints. We investigated this question on the CPG of the middle leg coxa-trochanter (CTr)-joint of the stick insect which is responsible for lifting and lowering the leg. We used a semi-intact preparation with an intact front leg stepping on a treadmill, and simultaneously stimulated load sensors of the middle leg. We found that middle leg load signals induce bursts in the middle leg depressor motoneurons (MNs). The same local load signals could also elicit rhythmic activity in the CPG of the middle leg CTr-joint when the stimulation of middle leg load sensors coincided with front leg stepping. However, the influence of front leg stepping was generally weak such that front leg stepping alone was only rarely accompanied by switching between middle leg levator and depressor MN activity. We therefore conclude that the impact of the local sensory signals on the levator-depressor motor system is stronger than the inter-segmental influence through front leg stepping