Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila

In Drosophila melanogaster, olfactory encoding in the mushroom body (MB)
involves thousands of Kenyon cells (KCs) processing inputs from hundreds
of projection neurons (PNs). Recent data challenge the notion of random
PN-to-KC connectivity, revealing preferential connections between
food-related PNs and specific KCs. Our study further uncovers a broader
picture—an L-shaped hybrid network, supported by spatial patterning:
food-related PNs diverge across KC classes, while pheromone-sensitive PNs
converge on γ KCs. α/β KCs specialize in food odors, while γ KCs integrate
diverse inputs. Such spatial arrangement extends further to the antennal
lobe (AL) and lateral horn (LH), shaping a systematic olfactory landscape.
Moreover, our functional validations align with computational predictions
of KC odor encoding based on the hybrid connectivity, correlating PN-KC
activity with behavioral preferences. Additionally, our simulations
showcase the network’s augmented sensitivity and precise discrimination
abilities, underscoring the computational benefits of this hybrid
architecture in olfactory processing.