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Area of Science:

  • Neuroscience
  • Computational Biology
  • Animal Behavior

Background:

  • Animal behavior emerges from sensory processing and motor control.
  • Modeling neural contributions to behavior is crucial for understanding sensorimotor transformations.

Purpose of the Study:

  • To develop a novel modeling approach for mapping internal neural network units to real neurons.
  • To predict behavioral changes from systematic neuronal perturbations.
  • To model sensorimotor transformations in Drosophila melanogaster males during visually guided social behavior.

Main Methods:

  • Introduced 'knockout training,' perturbing a deep neural network during training to mimic real neuronal perturbations.
  • Applied the model to Drosophila males performing complex social behaviors.
  • Identified specific neuronal cell types and their roles in behavior.

Main Results:

  • The model identified a one-to-one mapping between internal network units and real neurons.
  • Contrary to prior work, combinations of visual projection neurons, not discrete channels, drive male social interactions.
  • A rich population code, involving neurons in non-social behaviors, was found to drive female interactions.

Conclusions:

  • The framework consolidates behavioral effects from neural perturbations into a unified model.
  • Provides a stimulus-to-neuronal cell type-to-behavior map.
  • Enables future integration of brain wiring diagrams into the model.