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Van De Poll, M. N.

Publications and source records attributed to Van De Poll, M. N..

2 recordsLinked to original sources

A central steering circuit in Drosophila

The transformation of navigational variables into coordinated motor actions is a fundamental requirement for autonomous locomotion. The circuit and control mechanisms that underlie this transformation are not well understood. Here, we combine connectomics, neuronal activity perturbations, functional imaging and electrophysiology to identify a central steering circuit in Drosophila. This steering circuit operates in both goal-directed navigation and exploratory search and controls turning during both walking and flight, by implementing a hierarchical, stepwise transformation from navigation-related signals to motor output. At the top of the hierarchy are the reciprocally connected DNa03 and LAL013 neurons. DNa03 integrates lateralized sensory and internal information to generate an effector-agnostic steering signal during goal-directed navigation. LAL013 promotes exploratory turning in response to bilateral input from ExR7 neurons of the central complex, which encode the uncertainty of the flys internal heading estimate. Both DNa03 and LAL013 provide input to a set of hierarchically organized descending neurons, at the top of which is DNa11. DNa11 targets leg motor circuits directly as well as indirectly through subordinate descending neurons, and its activation coordinately changes the stepping directions of all six legs to generate rapid saccadic turns. In contrast to DNa03, DNa11 shows unilateral, delayed and low pass filtered activity during a turn. The signal transformation between DNa03 and DNa11 thus marks the transition from motor planning to the execution of a walking turn. Together, our results reveal a hierarchical circuit architecture that transforms navigational variables into abstract steering representations and ultimately into motor commands, providing a general framework for flexible control of behaviour across contexts and motor systems.

neuroscience↗

Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies

Sleep is observed in most animals, which suggests it subserves a fundamental process associated with adaptive biological functions. However, the evidence to directly associate sleep with a specific function is lacking, in part because sleep is not a single process in many animals. In humans and other mammals, different sleep stages have traditionally been identified using electroencephalograms (EEGs), but such an approach is not feasible in different animals such as insects. Here, we perform long-term multichannel local field potential (LFP) recordings in the brains of behaving flies undergoing spontaneous sleep bouts. We developed protocols to allow for consistent spatial recordings of LFPs across multiple flies, allowing us to compare the LFP activity across awake and sleep periods and further compare the same to induced sleep. Using machine learning, we uncover the existence of distinct temporal stages of sleep and explore the associated spatial and spectral features across the fly brain. Further, we analyze the electrophysiological correlates of micro-behaviours associated with certain sleep stages. We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extensions and show that spectral features of this sleep-related behavior differ significantly from those associated with the same behavior during wakefulness, indicating a dissociation between behavior and the brain states wherein these behaviors reside.

neuroscience↗