A realistic locomotory model of Drosophila larva for behavioral simulations

Abstract

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 topdown 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. Interindividual 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.

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

“…Our current research extends the present model towards a plastic spiking network model of the larva that can perform associative learning and reward prediction [19,125] inspired by recent modeling approaches in the adult [126,127]. Together with biologically realistic modeling of individual larva locomotion and chemotactic behavior [16] this will allow to reproduce behavioral [128][129][130][131][132] and optophysiological observations [64,133,134] and to generate testable hypothesis at the physiological and behavioral level.…”

“…Drawing inspiration from neural computation in the nervous systems of insects is particularly promising for developing neuromorphic computing paradigms. With their comparatively small brains ranging from ≈10 000 neurons in the fruit fly larva to ≈1 million neurons in the honeybee, insects are able to solve many formidable tasks such as the efficient recognition of relevant objects in a complex environment [10,11], perceptual decision making [12][13][14], or the exploration of unknown terrain and navigation [15][16][17][18][19]. They also show simple cognitive abilities such as learning, or counting of objects [20][21][22][23][24].…”

Prediction error drives associative olfactory learning and conditioned behavior in a spiking model ofDrosophila larva

“…4 B2). To this end we utilized a realistic model for the simulation of larval locomotion and chemotactic behavior [41] that utilizes the behavioral bias at the output of the MB model as a constant gain factor to modulate the locomotory behavior of individual larva towards or away from a spatially placed odor source in a virtual arena (see Methods).…”

“…Sensory clues can acquire the necessary predictive power to guide behavior through classical conditioning, a process potentially driven by reward/punishment PE [9,12], as observed in vertebrates [19,[32][33][34][35]. To test the biological plausibility of our proposed PE coding motif inn the insect MB we implemented a spiking network model of the Drosophila larva olfactory pathway, coupled with a simulation of locomotory behavior [41], to replicate larval conditioning experiments in a timeresolved manner. We demonstrate that our model of PE coding, in combination with synaptic homeostasis, results in saturating group-level and individual learning curves, where the slope and maximum of the learning curve are determined by the intensity of both the reward and the odor signal.…”

“…We demonstrate that this model can reproduce the experimentally observed findings on the acquisition of associations of odors with reinforcement in a time-resolved manner. To facilitate direct qualitative and quantitative comparisons with animal behavioral data we couple this model with a realistic locomotory model of the larva [41] that captures the effects of learned associations on chemotactic behavior in individual animals.…”