A realistic locomotory model of Drosophila larva for behavioral simulations.

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and top-down modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Inter-individual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

Adaptation of Drosophila larva foraging in response to changes in food distribution

When foraging, animals combine internal cues and sensory input from their environment to guide sequences of behavioral actions. Drosophila larva executes crawls, turns, and pauses to explore the substrate and find food sources. This exploration has to be flexible in the face of changes in the quality of food so that larvae feed in patches with favorable food and look for another source when the current location does not fulfill their nutritional needs. But which behavioral elements adapt, and what triggers those changes remain elusive. Using experiments and modeling, we investigate the foraging behavior of larvae in homogeneous environments with different food types and in environments where the food sources are patchy. Our work indicates that the speed of larval crawling and frequency of pauses is modulated by the food quality. Interestingly, we found that the genetic dimorphism in the foraging gene influences the exploratory behavior only when larvae crawl on yeast patches. While in a homogeneous substrate larvae maintain a turning bias in a specific orientation, in a patchy substrate larvae orient themselves towards the food when the patch border is reached. Therefore, by adapting different elements in their foraging behavior, larvae either increase the time inside nutritious food patches or continue exploring the substrate in less nutritious environments.

An olfactory pattern generator for on-demand combinatorial control of receptor activities

Olfactory systems employ combinatorial receptor codes for odors. Systematically generating stimuli that address the combinatorial possibilities of an olfactory code poses unique challenges. Here, we present a stimulus method to probe the combinatorial code, demonstrated using the Drosophila larva. This method leverages a set of primary odorants, each of which targets the activity of one olfactory receptor neuron (ORN) type at an optimal concentration. Our setup uses microfluidics to mix any combination of primary odorants on demand to activate any desired combination of ORNs. We use this olfactory pattern generator to demonstrate a spatially distributed olfactory representation in the dendrites of a single interneuron in the antennal lobe, the first olfactory neuropil of the larva. In the larval mushroom body, the next processing layer, we characterize diverse receptive fields of a population of Kenyon cells. The precision and flexibility of the olfactory pattern generator will facilitate systematic studies of processing and transformation of the olfactory code.

Leucokinins: Multifunctional Neuropeptides and Hormones in Insects and Other Invertebrates

Leucokinins (LKs) constitute a neuropeptide family first discovered in a cockroach and later identified in numerous insects and several other invertebrates. The LK receptors are only distantly related to other known receptors. Among insects, there are many examples of species where genes encoding LKs and their receptors are absent. Furthermore, genomics has revealed that LK signaling is lacking in several of the invertebrate phyla and in vertebrates. In insects, the number and complexity of LK-expressing neurons vary, from the simple pattern in the Drosophila larva where the entire CNS has 20 neurons of 3 main types, to cockroaches with about 250 neurons of many different types. Common to all studied insects is the presence or 1–3 pairs of LK-expressing neurosecretory cells in each abdominal neuromere of the ventral nerve cord, that, at least in some insects, regulate secretion in Malpighian tubules. This review summarizes the diverse functional roles of LK signaling in insects, as well as other arthropods and mollusks. These functions include regulation of ion and water homeostasis, feeding, sleep–metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. Other functions are implied by the neuronal distribution of LK, but remain to be investigated.

Anatomy and Neural Pathways Modulating Distinct Locomotor Behaviors in Drosophila Larva

The control of movements is a fundamental feature shared by all animals. At the most basic level, simple movements are generated by coordinated neural activity and muscle contraction patterns that are controlled by the central nervous system. How behavioral responses to various sensory inputs are processed and integrated by the downstream neural network to produce flexible and adaptive behaviors remains an intense area of investigation in many laboratories. Due to recent advances in experimental techniques, many fundamental neural pathways underlying animal movements have now been elucidated. For example, while the role of motor neurons in locomotion has been studied in great detail, the roles of interneurons in animal movements in both basic and noxious environments have only recently been realized. However, the genetic and transmitter identities of many of these interneurons remains unclear. In this review, we provide an overview of the underlying circuitry and neural pathways required by Drosophila larvae to produce successful movements. By improving our understanding of locomotor circuitry in model systems such as Drosophila, we will have a better understanding of how neural circuits in organisms with different bodies and brains lead to distinct locomotion types at the organism level. The understanding of genetic and physiological components of these movements types also provides directions to understand movements in higher organisms.

A plausible mechanism for Drosophila larva intermittent behavior

The behavior of many living organisms is not continuous. Rather, activity emerges in bouts that are separated by epochs of rest, a phenomenon known as intermittent behavior. Although intermittency is ubiquitous across phyla, empirical studies are scarce and the underlying neural mechanisms remain unknown. Here we present the first empirical evidence of intermittency during Drosophila larva free exploration. We report power-law distributed rest-bout and log-normal distributed activity-bout durations. We show that a stochastic network model can transition between power-law and non-power-law distributed states and we suggest a plausible neural mechanism for the alternating rest and activity in the larva. Finally, we discuss possible implementations in behavioral simulations extending spatial Levy-walk or coupled-oscillator models with temporal intermittency.