FB2026_03 , released September 17, 2026
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Citation
Shiu, P.K., Sterne, G.R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., Simha, N., Kang, C.H., Yu, S., Kim, J.S., Dorkenwald, S., Matsliah, A., Schlegel, P., Yu, S.C., McKellar, C.E., Sterling, A., Costa, M., Eichler, K., Bates, A.S., Eckstein, N., Funke, J., Jefferis, G.S.X.E., Murthy, M., Bidaye, S.S., Hampel, S., Seeds, A.M., Scott, K. (2024). A Drosophila computational brain model reveals sensorimotor processing.  Nature 634(8032): 210--219.
FlyBase ID
FBrf0260565
Publication Type
Research paper
Abstract
The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain[1,2]. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity[3], to study circuit properties of feeding and grooming behaviours. 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[4]. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing[5]-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. 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, and accurately describes the circuit response upon activation of different mechanosensory subtypes[6-10]. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.
PubMed ID
PubMed Central ID
PMC11446845 (PMC) (EuropePMC)
Related Publication(s)
Note

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Use citizen science to turbocharge big-data projects.
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Secondary IDs
    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    Nature
    Title
    Nature
    Publication Year
    1869-
    ISBN/ISSN
    0028-0836
    Data From Reference
    Genes (1)