Biology subjects

Spiller, N.

Publications and source records attributed to Spiller, N..

2 recordsLinked to original sources

bioRxiv2023

Neural circuit mechanisms underlying context-specific halting in Drosophila.

Walking is a complex motor program involving coordinated and distributed activity across the brain and the spinal cord. Halting appropriately at the correct time is a critical but often overlooked component of walking control. While recent studies have delineated specific genetically defined neuronal populations in the mouse brainstem that drive different types of halting1-3, the underlying neural circuit mechanisms responsible for overruling the competing walking-state neural activity to generate context-appropriate halting, remain unclear. Here, we elucidate two fundamental mechanisms by which Drosophila implement context-appropriate halting. The first mechanism ("walk-OFF" mechanism) relies on GABAergic neurons that inhibit specific descending walking commands in the brain, while the second mechanism ("brake" mechanism) relies on excitatory cholinergic neurons in the nerve-cord that lead to an active arrest of stepping movements. Using connectome-informed models4-6 and functional studies, we show that two neuronal types that deploy the "walk-OFF" mechanism inhibit distinct populations of walking-promotion neurons, leading to differential halting of forward-walking or steering. The "brake" neurons on the other hand, override all walking commands by simultaneously inhibiting descending walking promoting pathways and increasing the resistance at the leg-joints leading to an arrest of leg movements in the stance phase of walking. We characterized two ethologically relevant behavioral contexts in which the distinct halting mechanisms were used by the animal in a mutually exclusive manner: the "walk-OFF" pathway was engaged for halting during feeding, and the "brake" pathway was engaged for halting during grooming. Furthermore, this knowledge of the neural targets and mechanisms for halting, allowed us to use connectomics to predict novel halting pathways that could be relevant in other behavioral contexts.

neuroscience↗

bioRxiv2023

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

neuroscience↗